GPT-6 vs Claude Opus 5.5: Frontier AI Models

At a glance

Claude Opus 5.5 and GPT-6 are frontier models released in 2026 for knowledge work, coding, and agentic tasks. Opus 5.5 leads on published agentic coding benchmarks and costs significantly less per token for cached work. GPT-6 includes multiple variants with different pricing and capability profiles.

Feature	GPT-6	Claude Opus 5.5
Maker	OpenAI	Anthropic
Release date	2026	September 22, 2026
Access	API	API, Claude Platform, AWS, Google Cloud, Microsoft, enterprise
Context window	Not published	1M tokens
Max output	Not published	128K tokens
Input pricing	$10/M (Astra); $2/M (Sol)	$4/M
Output pricing	$50/M (Astra); $10/M (Sol)	$20/M
Cache pricing	$1/M (Astra)	$0.20/M
Best for	General-purpose reasoning	Agentic coding, knowledge work, long-running tasks

GPT-6: strengths and limits

OpenAI’s GPT-6 line includes multiple models: Astra, Sol, and Luna. Each addresses different workloads and budgets. GPT-6 Astra is positioned as the highest-capability general model. On published benchmarks, GPT-6 Astra scores 53.3% on FrontierCode v1.1 agentic coding and 57.9% on Terminal-Bench 4.0, trailing Claude Opus 5.5 on both. OpenAI reports that GPT-6.1 Sol delivers substantial scientific capability at over 75% cost reduction versus Astra.

The GPT-6 line’s main strength is pricing flexibility. According to OpenAI, GPT-6.1 Sol costs $5.47 per task on average, providing a lower-cost option for organizations seeking frontier capability at reduced spend.

However, OpenAI has not published context window size, maximum output length, or complete benchmark data for GPT-6 variants. This makes direct capability comparison difficult for specific use cases. The models also appear optimized for different tasks: Sol is positioned for cost efficiency, while Astra carries the flagship positioning.

Claude Opus 5.5: strengths and limits

Claude Opus 5.5 leads on agentic coding and knowledge work benchmarks. On Terminal-Bench 4.0, Opus 5.5 scores 66.4%, compared with 57.9% for GPT-6 Astra. On FrontierCode v1.1, Opus 5.5 reaches 54.4% versus 53.3% for Astra. On CursorBench 4.0, Opus 5.5 achieves 57.8% with no published GPT-6 Astra result for comparison.

Anthropic reports that Opus 5.5 generates output more than 30% faster than Opus 5 at default settings. An early tester completed a 680,000-line code migration in less than a day, work described as typically taking an engineering team weeks. On web application optimization, Opus 5.5 succeeded 39 of 40 times in cutting load times across every page, while Opus 5 made smaller improvements that altered app behavior.

Opus 5.5 costs 40% less to run than Opus 5 on typical workloads. Input and output pricing is $4 and $20 per million tokens, 20% less than Opus 5. Cache reads cost $0.20 per million tokens, 60% less. Anthropic also increased usage limits on Pro, Max, Team, and enterprise plans and added a rate limit reset users can save and apply when needed.

On safety, Opus 5.5 achieves the best scores on Anthropic’s automated behavioral audit of any model tested to date. It is less likely to take hard-to-reverse actions or act outside defined boundaries and shows greater resistance to prompt injection than Opus 5. Anthropic notes that at this capability level, benchmark margins are less reliable for predicting real-world differences, and the gap between Opus 5.5 and Claude Fable 5.1 is narrower than scores suggest.

Limitations include alignment testing still having limits, particularly on longer tasks and impossible tasks. Access to Opus 5.5 for biology and cybersecurity requires verification through Anthropic’s Life Sciences or Cyber Verification programs.

Price and access

Claude Opus 5.5 pricing is $4 per million input tokens and $20 per million output tokens via the Claude API. Cache reads are $0.20 per million. Anthropic offers Opus 5.5 through the Claude Platform, via API, and through AWS, Google Cloud, Microsoft, and enterprise partnerships. The model has a 1M token context window and 128K token max output.

GPT-6 pricing varies by model. GPT-6 Astra starts at $10 per million input tokens and $50 per million output tokens, with cached input at $1 per million. GPT-6.1 Sol costs $2 per million input and $10 per million output. OpenAI reports Sol costs $5.47 per task on average. OpenAI has not published context window or max output specifications for either model.

For agentic and long-running tasks where cache reads dominate costs, Opus 5.5’s $0.20 per million cached token rate is substantially lower than Astra’s $1 per million. For one-off reasoning tasks without caching, GPT-6 Sol’s $2 input and $10 output pricing undercuts Opus 5.5. For flagship general capability, Astra’s per-token costs are higher, though it may justify spend in specialized domains where published benchmarks are incomplete.

Which one should you choose

Choose Claude Opus 5.5 if you run agentic systems, long-running code tasks, or repeated queries on the same data. Opus 5.5’s lower cache pricing ($0.20/M versus $1/M for Astra) makes it cheaper for these workflows even though per-token input and output pricing is higher. Choose Opus 5.5 also if you need published benchmarks, transparent safety evaluation, and established access to sensitive domains like biology and cybersecurity research.

Choose GPT-6 if you need the lowest per-token input and output cost for one-off reasoning tasks without caching. GPT-6.1 Sol’s $2/$10 pricing is half Opus 5.5’s $4/$20. Choose GPT-6 Astra if you have evidence it performs better on your specific use case, though published benchmarks show Opus 5.5 leading on agentic coding and knowledge work. You may also want to compare with lighter-weight models like Claude Haiku 5.5 for cost-sensitive applications.

Common questions

Is Claude Opus 5.5 faster than GPT-6?

Third-party benchmarks report Claude Opus 5.5 at 95 tokens per second (Max, Default Fallback) versus GPT-6 Astra at 62 tokens per second (Max). Anthropic also reports that Opus 5.5 generates output more than 30% faster than Opus 5.

Is GPT-6 cheaper than Claude Opus 5.5?

According to OpenAI, GPT-6.1 Sol costs $5.47 per task on average versus $23.21 for Opus 5.5. However, cost comparison depends on your workload. For one-off queries, GPT-6 Sol at $2/$10 is half Opus 5.5’s $4/$20. For agentic and repeated tasks, Opus 5.5’s cache at $0.20/M is significantly cheaper than Astra’s $1/M.

Does Claude Opus 5.5 perform better on coding than GPT-6?

On published agentic coding benchmarks, Claude Opus 5.5 leads. On Terminal-Bench 4.0, Opus 5.5 scores 66.4% compared with GPT-6 Astra’s 57.9%. On FrontierCode v1.1, Opus 5.5 reaches 54.4% versus Astra’s 53.3%. On CursorBench 4.0, Opus 5.5 achieves 57.8% with no published result for Astra.

The conclusion

Claude Opus 5.5 and GPT-6 serve different optimization goals. If your work involves repeated queries, agentic loops, or long-running coding tasks where prompt caching matters, Opus 5.5’s lower cache pricing and faster output make it the better choice despite higher base per-token costs. Opus 5.5 also benefits from published safety evaluation, transparent benchmarks, and established verification programs for sensitive work.

GPT-6 wins on cost-per-task for one-off work with GPT-6.1 Sol, and for organizations already invested in the OpenAI ecosystem. However, GPT-6’s missing published specs for context, max output, and full benchmark coverage make it harder to predict performance on specific tasks. For teams balancing cost and capability across multiple models, frameworks and inference optimizations can significantly reduce serving costs.

The real choice hinges on your workflow pattern. Agentic and cached work favors Opus 5.5. Single-task reasoning on a budget favors GPT-6 Sol. For either, run pilots on your actual workload; published benchmarks at this capability level are a less reliable guide than testing on real tasks.