Claude Opus 5.5 vs GPT-6 Sol: What’s Actually Different?

On September 22, 2026, Anthropic and OpenAI released competing models within hours of each other, both emphasizing cost efficiency alongside capability. Claude Opus 5.5 is Anthropic’s new flagship, while GPT-6 Sol positions itself as a mid-tier model under OpenAI’s flagship GPT-6 Astra. The comparison matters not because one is universally superior, but because they embody different strategies for the cost-capability tradeoff. Both models target demanding work. Both cut prices. But they occupy different positions within their respective families, and understanding that difference is the key to knowing which fits your workload.

This article compares what each model actually offers beyond the launch-day benchmarks, separates token pricing from real task economics, and explains why the meaningful comparison requires more nuance than a single benchmark score.

Claude Opus 5.5 vs GPT-6 Sol at a Glance

Opus 5.5 is Anthropic’s new flagship with strong emphasis on token efficiency and agentic work. GPT-6 Sol is a lower-tier model within OpenAI’s family, positioned to deliver substantial capability at half the token price of Opus 5.5. The gap between them is narrower than raw capability tiers might suggest, partly because Opus 5.5 achieves its cost savings through both lower pricing and lower token consumption per task.

The Biggest Difference Is Where Each Model Sits in Its AI Family

Claude Opus 5.5: Anthropic’s New Flagship

Claude Opus 5.5 is the first model in Anthropic’s new 5.5 family, positioned as the company’s leading model for demanding work. Anthropic reports that Opus 5.5 achieves the level of Claude Fable 5.1 (an earlier frontier model) on most tasks while costing 40% less to run than Opus 5, the previous generation flagship from July 2026.

The efficiency gains come from two sources. First, pricing is lower: input tokens drop 20% from $5 to $4 per million, output tokens drop 20% from $25 to $20 per million, and cache reads drop 60% from $0.50 to $0.20 per million. Second, Anthropic reports that Opus 5.5 uses fewer tokens per task and generates output more than 30% faster than Opus 5, meaning the actual per-task cost falls faster than the per-token price suggests.

Anthropic emphasizes agentic coding (multi-step terminal work), computer use, and knowledge work as Opus 5.5’s strongest areas. Early testers reported completing large codebase migrations, writing financial models, and conducting long-running research tasks with fewer model turns and less rework than previous Opus versions.

GPT-6 Sol: OpenAI’s Cost-Efficient GPT-6 Tier

GPT-6 Sol is positioned below OpenAI’s flagship GPT-6 Astra, part of OpenAI’s strategy to distribute frontier-family capability across tiers optimized for different cost and speed profiles. OpenAI trained GPT-6 Sol with similar methods as Astra but as a more efficient variant.

On token price alone, GPT-6 Sol is aggressive: $2 per million input (half of Opus 5.5) and $10 per million output (also half). The cache read price is identical to Opus 5.5 at $0.20 per million tokens. OpenAI reports these as permanent pricing, not a launch promotion, and states they represent a 50% reduction compared to the prior GPT-5.6 Sol model.

OpenAI positions GPT-6 Sol for professional work, difficult coding tasks, agentic workflows, and computer use. The model also benefits from OpenAI’s recent improvements to prompt caching, which help agents reuse context more efficiently.

Why the Difference in Positioning Matters

Comparing Opus 5.5 (a flagship model) directly with GPT-6 Sol (a mid-tier model within another family) requires recognizing that benchmark margins become less reliable guides at these capability levels, as Anthropic itself notes. A 5-point difference on a coding benchmark between a flagship and a mid-tier model is not the same as a 5-point difference between a mid-tier and a budget model.

Opus 5.5 targets teams that need maximum capability and are willing to pay for it. GPT-6 Sol targets teams that accept slightly lower per-task capability in exchange for substantially lower per-task cost. The choice between them is not “which is better overall,” but “which capability-cost profile suits our workload.”

Claude Opus 5.5 vs GPT-6 Sol Pricing

GPT-6 Sol’s published fresh-token pricing is exactly half that of Opus 5.5: $2 vs $4 for input, $10 vs $20 for output. This is a substantial headline difference. However, both models charge identical prices for cached input reads at $0.20 per million tokens. For workloads that re-read the same context heavily (typical in agentic and coding work), the pricing gap narrows considerably because the per-task cost depends on the mix of fresh reads, cached reads, and output tokens.

A practical example: for a monthly agentic workload of one million input tokens with 90% served from cache, plus 100,000 output tokens, Opus 5.5 costs approximately $2.58, and GPT-6 Sol costs approximately $1.38, making Sol roughly 47% cheaper for this specific workload. At a heavier scale of 10 million input tokens and one million output tokens, the gap becomes real money: approximately $26 for Opus 5.5 versus $14 for GPT-6 Sol.

One important pricing boundary: OpenAI charges double the input token rate and double the cache rate (plus 1.5x output) for any single request exceeding 272,000 input tokens on GPT-6 Sol and Luna. Anthropic has no equivalent documented surcharge on Opus 5.5 pricing. For long-context workloads, this threshold can eliminate GPT-6 Sol’s price advantage entirely if prompts regularly exceed the boundary.

Token Price vs Real AI Task Cost

Token price and cost per completed task are not the same thing, and conflating them is one of the most common mistakes when evaluating models. Two models with different token prices can end up costing the same per completed task if one requires fewer model turns, less output, fewer tool calls, or lower retry rates.

Several factors affect real task cost independent of token pricing:

Model turns and reasoning effort. A model that reaches the right answer in three turns at high reasoning effort may cost less per task than a model requiring five turns at lower effort, even if the per-token price is lower.

Output length and efficiency. A more concise model outputs fewer tokens per task. Opus 5.5 is reported to output 30% faster and use fewer tokens per task than Opus 5, compounding the benefit of lower pricing.

Cached context reuse. Agentic and coding workloads often re-read the same large context dozens of times per task. When cache read pricing is identical ($0.20 per million tokens for both models), the fresh-token price difference becomes a smaller fraction of total cost.

Reasoning settings. Both models offer adjustable reasoning effort. A task completed at lower effort with Opus 5.5 might cost less than the same task at higher effort with GPT-6 Sol, despite Sol’s lower headline pricing.

Task completion rate. A model that solves the task correctly on the first attempt saves the cost of retries. Higher capability sometimes translates directly to lower cost per successful completion.

Benchmark methodologies. Published benchmarks compare models under specific effort settings and conditions. Your actual workload may differ in ways that change the cost picture.

Anthropic’s claim that Opus 5.5 costs 40% less than Opus 5 incorporates both pricing and token efficiency. If Opus 5.5 uses 25% fewer tokens per task and has 20% lower per-token pricing, the combined effect is roughly 40% lower total cost. GPT-6 Sol’s advantage is primarily price (50% lower per fresh token) rather than efficiency, since there’s no published evidence it uses fewer tokens per task than Opus 5.

Claude Opus 5.5 vs GPT-6 Sol Benchmarks

There is not yet a single independent benchmark suite that directly compares Claude Opus 5.5 and GPT-6 Sol under identical conditions. Both companies have published benchmark results, but they often evaluate different model versions against different competitors, making direct comparison difficult. The following summary distinguishes between what each company reports and identifies the models and conditions involved.

Agentic Coding

Anthropic’s reported results for Opus 5.5 on Terminal-Bench 4.0 (a test of complex multi-step terminal tasks) show 66.4% accuracy at xhigh effort. Anthropic’s benchmark table includes GPT-5.6 Sol (not GPT-6 Sol) at 37.3%. This is a significant gap, but it compares the new Opus 5.5 against the previous generation of OpenAI’s sol-tier model, not the new GPT-6 Sol released the same day.

OpenAI’s reported results for GPT-6 Sol on FrontierCode v1.1 Main (a test of whether code changes would be merged into real codebases) show that “GPT-6 Sol improves substantially over GPT-5.6 Sol, and is able to match Claude Fable 5.1 xhigh at much lower cost.” Anthropic reports Opus 5.5 at 54.4% on the same benchmark. OpenAI does not publish a direct GPT-6 Sol vs Opus 5.5 comparison on FrontierCode.

On DeepSWE v1.1 (complex software-engineering tasks in real codebases), OpenAI reports GPT-6 Sol at max effort scores 68.8%, “within 1.1 percentage points of Claude Fable 5’s highest score in the evaluation, 69.9% at xhigh effort, at approximately 80% lower cost per task.” Fable 5 and Opus 5.5 are different models; Anthropic does not publish Opus 5.5’s DeepSWE score.

The honest assessment: coding benchmarks show both models performing at high levels, but the published comparisons are fragmented. Opus 5.5 leads on some terminal coding benchmarks; GPT-6 Sol matches or beats Fable 5.1 on others. Independent testing under identical conditions is needed for a definitive head-to-head comparison.

Professional and Knowledge Work

On AutomationBench (real business workflows across 47 tools in sales, marketing, finance, HR, etc.), OpenAI reports GPT-6 Sol at high effort scores 33.2%, outperforming “Claude Opus 5 at max effort at just 9% of Opus 5’s cost per task” and exceeding Claude Fable 5.1 at far lower cost. Anthropic reports Opus 5.5 at 40.0% on AutomationBench, which is higher than OpenAI’s reported GPT-6 Sol score, though the comparison is not direct (different effort levels, possibly different conditions).

On Agents’ Last Exam (complex professional workflows across 55 sub-industries), OpenAI reports GPT-6 Sol at max effort scores 56.4%, “above Claude Opus 5’s highest score in the evaluation at 60% lower cost per task.” Anthropic does not publish Opus 5.5’s score on this benchmark.

On GDPval-AA v2.1 (real-world professional work across 44 occupations), Anthropic reports Opus 5.5 at 1846 Elo, higher than Fable 5.1 and Opus 5. Anthropic’s benchmark table shows GPT-5.6 Sol (the previous generation) at 1588 Elo, but GPT-6 Sol results are not published by either company on this benchmark.

Computer Use

On OSWorld 2.0 offline (long-horizon computer-use workflows), Anthropic reports Opus 5.5 at 81.8% partial reward. OpenAI reports GPT-6 Sol at xhigh effort achieves 60.5% on the same benchmark (offline set), described as “similar score to Claude Opus 5 at medium effort, 60.5% versus 60.3%, at approximately 80% lower cost per task.” These results suggest Opus 5.5 leads in computer use capability, with GPT-6 Sol offering lower cost at reduced performance.

What the Benchmark Numbers Actually Mean

Benchmark results are useful signals, but they have substantial limitations when comparing these two models:

Different effort settings. Opus 5.5 results sometimes use xhigh or max reasoning effort, while GPT-6 Sol results may use different settings. A higher score at higher effort is not always a fairer comparison than a lower score at lower effort with lower cost.

Different benchmark harnesses. Anthropic and OpenAI may run benchmarks slightly differently. Anthropic notes that safeguard interventions on some tasks (cybersecurity, biology) affect benchmark scores. Zapier’s AutomationBench results for Anthropic models omit fallback model costs, which affects cost-per-task calculations.

Vendor-reported results. Both sets of benchmarks come from the companies developing the models, not independent evaluators. Vendor results are credible but naturally emphasize favorable comparisons.

Different model versions in comparisons. Anthropic’s benchmark table includes GPT-5.6 Sol (the prior generation), not GPT-6 Sol. Direct Opus 5.5 vs GPT-6 Sol comparisons are incomplete.

Standard error and noise. Several benchmarks include error margins (Terminal-Bench 4.0 has ±2.6 pts for Opus 5.5, ±1.6–2 pts for others). Small performance differences may not be statistically significant.

The reasonable conclusion after one day of data: performance between Opus 5.5 and GPT-6 Sol is close enough on many benchmarks that real-world cost difference, not benchmark margins, should drive the decision for most workloads.

Claude Opus 5.5 vs GPT-6 Sol for Coding

For developers and teams building coding agents, the comparison centers on capability, cost efficiency, and task completion speed.

Repository-scale work. Anthropic reports that early testers used Opus 5.5 to complete a 680,000-line code migration in less than a day, work that would normally take an engineering team weeks. Another tester audited and fixed a 200,000-line codebase in under three hours using Opus 5.5, whereas Opus 5 took over 20 hours and used 2.5x as many tokens. On FrontierCode, Anthropic reports that Opus 5.5 at default effort beats GPT-6 Astra at roughly 20% of the cost per task. These are Anthropic-reported results from early testers, not independent benchmarks.

Coding at scale with cost constraints. OpenAI reports that GPT-6 Sol “is able to match Claude Fable 5.1 xhigh at much lower cost” on FrontierCode. On DeepSWE v1.1, GPT-6 Sol at max effort scores 68.8%, within 1.1 percentage points of Claude Fable 5’s best score but at approximately 80% lower cost per task. For teams running high-volume coding agents where cost per task is the primary constraint, GPT-6 Sol’s pricing advantage may outweigh slight capability differences.

Coding workflows and tool integration. Both models support agentic coding through various platforms. Anthropic highlights integration with GitHub Copilot, VS Code, and Claude Code. OpenAI emphasizes Codex and internal usage at $600 median daily token cost for researchers. The models themselves are primarily distributed through API and proprietary platforms, so tool integration depends on which ecosystem you already use.

Token efficiency in long sessions. Opus 5.5’s reported 30% faster output generation and lower tokens-per-task may compound advantages in long coding sessions where cumulative token cost adds up. GitHub reported that Opus 5.5 “solved more terminal tasks than Opus 5 in less than half the steps” in VS Code testing.

Best fit for coding: Choose Opus 5.5 for repository-scale refactors, long-running coding sessions, and tasks where a failed attempt is expensive (retry costs compound quickly). Choose GPT-6 Sol for high-volume, lower-risk coding tasks where cost per task dominates, and acceptable-quality code is sufficient.

Agentic Workflows and Tool Use

Both models are designed to handle multi-step tasks with repeated reasoning and tool calls.

Tool calling and reasoning loops. Opus 5.5 is reported to “delegate to subagents far more effectively and check its own work in creative ways” by early testers. GPT-6 Sol’s cost advantage is most pronounced when the same context is reused many times per task; agents that repeatedly re-read system instructions or context benefit from both models’ $0.20 per million cache read pricing.

Prompt caching improvements. OpenAI reports improvements to prompt caching for GPT-6, enabling “higher cache hit rates by default, helping agents reuse more context, respond faster, and benefit from discounts of 90% on cached input-token reads.” Developers can now adjust reasoning effort and tool availability without breaking cache reuse, and monitor caching performance through a dashboard. Anthropic does not provide equivalent visibility tools but reports that cache reads make up the majority of agentic work costs, suggesting strong cache behavior.

Long-running agents. Opus 5.5 is reported to handle autonomous work over many hours without drift. One early tester reported Opus 5.5 “stayed on task for over 18 hours defining how our services talk to each other and working out how each one should apply that.” For workflows where the cost of task failure (debugging a diverged agent) is high, Opus 5.5’s reported reliability advantage may justify higher per-task cost.

Scaling at cost. GPT-6 Sol’s 50% lower fresh-token pricing makes it rational for bulk agent work: high-volume classification, summarization, data extraction, and background processing where task quality is acceptable but cost is critical. The cache read price is identical to Opus 5.5, so both models are equally efficient on the cache-heavy portion of agent workloads.

Computer Use and Real-World Workflows

Computer use (an agent interacting with desktop interfaces, web browsers, and software) is an area where capability differs between the models.

Capability gap. On OSWorld 2.0 offline, Anthropic reports Opus 5.5 at 81.8% partial reward, while OpenAI reports GPT-6 Sol at 60.5% at xhigh effort. This 21-point gap is substantial and suggests Opus 5.5 is more reliable at complex desktop automation tasks. OpenAI notes that “GPT-6 Astra remains the world’s best model for computer use,” positioning GPT-6 Sol as a more cost-efficient but less capable alternative.

When cost trumps capability. GPT-6 Sol still exceeds its predecessor, GPT-5.6 Sol, on computer use tasks and does so at much lower cost. Teams running lightweight browser automation or simple desktop workflows may find GPT-6 Sol adequate while cutting costs substantially.

Agentic computer work. Opus 5.5 is positioned for autonomous long-running workflows that interact with software systems, relying on users to verify critical actions. GPT-6 Sol is better suited to shorter, higher-level automation tasks where failures are inexpensive to retry.

Reasoning and Effort Controls

Both models offer adjustable reasoning settings that affect accuracy, latency, and token consumption.

Opus 5.5’s adaptive thinking. Anthropic implemented adaptive thinking with effort levels (low, medium, high, xhigh, max). The company reports that at default (medium) effort, Opus 5.5 matches other models’ performance at higher effort levels. On Terminal-Bench 4.0, Opus 5.5 at xhigh effort scores 66.4%. On various benchmarks, Opus 5.5 at medium effort beats competitors at max effort while using fewer tokens.

GPT-6 Sol’s reasoning effort. OpenAI allows reasoning effort adjustment mid-conversation without breaking cache reuse. The model operates at different effort levels, though OpenAI’s benchmark comparisons use varying effort settings, making it difficult to isolate the effort parameter’s effect.

Cost implications. Higher reasoning effort increases token consumption and latency but may reduce retry rates for difficult tasks. Lower effort is faster and cheaper but may require more iterations. The real cost-per-task calculation depends on task difficulty and acceptable error rates.

Best practice. Test both models at different effort levels on representative tasks from your workload. A model that solves 80% of tasks at low effort with 10% cost may be cheaper than a model that solves 95% at max effort with 3x token cost.

Context Window and Long-Context Work

Both models support approximately one million token context windows, sufficient for long documents, large codebases, and extended conversations.

Opus 5.5: 1 million tokens official context window.

GPT-6 Sol: 1.05 million tokens official context window.

The practical difference is negligible. Both are sufficient for multi-file code repositories and long research documents. Neither provides a compelling advantage in raw context window size.

Prompt caching implications. For long-context workloads that reuse the same large context repeatedly (common in agentic work), both models charge $0.20 per million for cache reads, meaning the cache pricing advantage is identical. The difference comes from fresh-token pricing and output efficiency.

Length surcharges. OpenAI charges 2x the input rate and cache rate above 272K tokens for a single request on GPT-6 Sol. Anthropic has no equivalent published surcharge. Teams with prompts consistently above 272K should factor this into cost projections.

Claude Opus 5.5 vs GPT-6 Sol for Research and Knowledge Work

Both models are positioned as capable research and analysis tools, though with different strengths.

Factuality and source accuracy. OpenAI reports that GPT-6 Sol “makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost” on internal factuality evaluations. Anthropic reports that 16 out of 18 of Opus 5.5’s reports on a financial analysis task cleared quality standards that would have rejected invented figures, while Opus 5 failed in all attempts. Both models show factuality improvements, but the evaluations are not directly comparable.

Long-horizon research tasks. Anthropic reports Opus 5.5 successfully completing 18-hour research sessions with consistent output quality. GDPval-AA v2.1 results show Opus 5.5 at 1846 Elo on professional work, ahead of all other tested models. For research teams running overnight analysis jobs, Opus 5.5’s reported reliability and token efficiency advantage may justify higher per-task cost.

Cost-sensitive research. GPT-6 Sol’s 50% price advantage appeals to teams conducting high-volume literature reviews, data collection, or summarization where some acceptable error rate is tolerable. OpenAI reports that on AutomationBench (business workflows), GPT-6 Sol at high effort exceeds Claude Fable 5.1’s performance at far lower cost.

Knowledge base updates and maintenance. Repeated context retrieval and fact-checking are common in knowledge work. Both models charge identical cache read prices at $0.20 per million tokens, so the cost advantage of GPT-6 Sol applies primarily to new analysis and output generation, not context re-reading.

Which Model Fits Which Workflow

There is no universal winner. The choice depends on your specific tradeoffs.

Best fit for complex coding and large-scale refactoring: Opus 5.5. The combination of higher capability per benchmark, reported token efficiency, and faster output generation makes it the safer choice for repository-scale work where a failed attempt costs hours of debugging. Early tester results from GitHub, Stripe, and others consistently report fewer token consumption and faster task completion.

Best fit for high-volume API workloads: GPT-6 Sol. At exactly half the fresh-token price of Opus 5.5 with identical cache read pricing, GPT-6 Sol is the rational choice for bulk classification, summarization, and agent loops at scale. If your workload is cost-constrained and capable of tolerating occasional model variation, the price difference is free money.

Best fit for agentic workflows with long-running sessions: Opus 5.5. The combination of reported reliability, output efficiency, and cost-per-task advantages in long autonomous tasks justifies the higher per-token price. Agents that run unattended for hours and large-context reuse particularly benefit from Opus 5.5’s token efficiency.

Best fit for enterprise teams already using Claude tools: Opus 5.5. Integration with Claude Code, Cowork, and existing API setups means switching cost is minimal. The subscription improvements (increased usage limits, rate limit reset) are additional benefits independent of model quality.

Best fit for enterprise teams already using OpenAI platforms: GPT-6 Sol. Existing integration with ChatGPT Work, Codex, and OpenAI API means no switching cost. The model’s strong performance on business workflows and coding tasks, combined with aggressive pricing, makes it compelling within the OpenAI ecosystem.

Best fit for cost minimization with acceptable quality trade-off: GPT-6 Luna. At $0.10 per million input and $0.50 per million output, Luna (OpenAI’s even cheaper tier) handles light preprocessing, summarization, and quick questions at negligible cost. Available free-tier to all ChatGPT users.

What the Official Benchmarks Do Not Tell You

Published benchmarks provide useful comparative data but come with important caveats:

Production conditions vs research conditions. Benchmarks run in controlled harnesses with specific system prompts, tool sets, and fallback strategies. Your production workload may differ in prompt style, tool availability, or acceptable failure rates.

Vendor testing vs independent evaluation. Both companies report their own benchmark results. Anthropic explicitly notes that safeguard interventions (fallback to Opus 4.8 on some cybersecurity tasks, for example) reduce published scores. Independent testing is not yet available.

Effort setting variability. Comparing a model’s max effort result against another model’s high effort result is not apples-to-apples. Effort settings trade off accuracy, speed, and token consumption. Neither company clearly standardizes effort across comparisons with competitor models.

Long-context and reasoning overhead. Many benchmarks do not fully capture the cost of long-context reasoning. A model that spends more tokens on reasoning but gets the answer right on first attempt may cost less per task than a model that’s cheaper per token but requires retries.

Benchmark specialization. Some benchmarks favor certain model types. Terminal-Bench favors terminal and CLI reasoning. GDPval-AA tests professional work across 44 occupations. OSWorld tests computer use. Excelling on one benchmark does not guarantee equivalent advantage on your specific tasks.

Missing comparisons. Anthropic’s benchmarks include GPT-5.6 Sol (the prior generation), not GPT-6 Sol (the new model). OpenAI does not publish GPT-6 Sol scores on several Anthropic benchmarks. A complete head-to-head comparison does not yet exist.

The practical implication: treat published benchmarks as evidence but not as a complete substitute for testing the models on a representative sample of your actual workload.

Claude Opus 5.5 vs GPT-6 Sol: Key Differences

Final Takeaway

Claude Opus 5.5 and GPT-6 Sol represent two different answers to the same question: how to deliver frontier-adjacent capability at lower cost than previous generations. Opus 5.5 is Anthropic’s flagship with emphasis on token efficiency and high-capability workloads. GPT-6 Sol is a mid-tier OpenAI model positioned for cost-sensitive, high-volume work.

The meaningful comparison is not which model is universally better, but which cost-capability profile fits your specific constraints. Opus 5.5 excels at complex coding, long-horizon reasoning, and tasks where capability per task justifies higher cost. GPT-6 Sol excels at bulk workloads, business automation, and scenarios where acceptable quality at half the price is the rational choice.

Published benchmarks are incomplete and fragmented. Neither company has published direct head-to-head testing on all relevant benchmarks. The best decision framework starts with pricing and real-world token usage patterns specific to your workload, not with benchmark scores alone.

For high-stakes coding, complex reasoning, or mission-critical agentic work, Opus 5.5 is the safer choice. For cost-constrained production workloads where bulk processing, summarization, or light automation is the task, GPT-6 Sol’s 50% fresh-token price advantage is compelling. Test both models on representative samples of your workload before switching production systems.

The larger trend matters more than either individual model. Two years ago, frontier-quality capability meant frontier pricing. As of September 22, 2026, both companies cut prices substantially while maintaining capability. The effective cost of capable AI dropped by roughly half in a single day. That shift matters more than any single benchmark comparison.

Frequently Asked Questions

Is Claude Opus 5.5 better than GPT-6 Sol?

Not universally. Opus 5.5 leads on complex coding and long-running tasks where capability per task matters. GPT-6 Sol excels at cost-sensitive workloads where acceptable-quality results at half the price are preferable to maximum capability at higher cost. The choice depends on your workload priorities.

Is GPT-6 Sol cheaper than Claude Opus 5.5?

On fresh tokens, yes, exactly 50% cheaper ($2 vs $4 per million input, $10 vs $20 per million output). On cached input reads, they’re identical at $0.20 per million. For cache-heavy agentic workloads, GPT-6 Sol’s overall cost advantage is roughly 47% per task based on typical usage patterns.

Which is better for coding, Claude Opus 5.5 or GPT-6 Sol?

Opus 5.5 leads on repository-scale coding and complex refactors, as shown by benchmarks and early tester reports. GPT-6 Sol performs well on coding tasks and is substantially cheaper, making it rational for high-volume, lower-risk coding work. For critical, high-stakes coding projects, Opus 5.5 is the safer choice.

How much does Claude Opus 5.5 cost?

$4 per million input tokens and $20 per million output tokens. Cache reads are $0.20 per million and cache writes are $5 per million. For a typical agentic workload (1M tokens input, 90% cached), Opus 5.5 costs approximately $2.58 per task.

How much does GPT-6 Sol cost?

$2 per million input tokens and $10 per million output tokens, with cache reads at $0.20 per million. For the same typical agentic workload, GPT-6 Sol costs approximately $1.38 per task, about 47% less than Opus 5.5.

What is the context window of GPT-6 Sol?

1.05 million tokens, similar to Opus 5.5’s 1 million token context window. Both are sufficient for multi-file codebases and long documents.

What is Claude Opus 5.5 good for?

Agentic coding, computer use, knowledge work, long-running research tasks, and workloads where output quality per task is the primary constraint. Early testers report strong performance on 680,000-line code migrations, financial analysis, and legal research.

What is GPT-6 Sol good for?

Professional work, business automation, coding at scale, and agentic workflows where cost per task dominates. The model handles classification, summarization, data extraction, and multi-step business workflows efficiently at low cost.

How do Claude Opus 5.5 and GPT-6 Sol compare for AI agents?

Both support agentic workflows. Opus 5.5 is more reliable for long-running autonomous tasks and uses fewer tokens per task. GPT-6 Sol is more cost-efficient for high-volume agent loops and benefits from recent caching improvements. For agents running unattended for hours, Opus 5.5 is preferable. For bulk classification or summarization agents, GPT-6 Sol is rational.

Which model should developers use for production AI workflows?

Test both on representative samples of your workload. Opus 5.5 is the safer choice for mission-critical work, complex reasoning, and long-running tasks. GPT-6 Sol is rational for cost-constrained production workloads where acceptable-quality results matter more than maximum capability. Ecosystem switching costs (existing Claude or OpenAI integration) should also factor into the decision.