Claude Opus 5.5 vs GPT-6: comparing coding, price and AI agents

In September 2026, both Anthropic and OpenAI upgraded their flagship AI models. Claude Opus 5.5 leans hard into coding, long-running agents and professional work, while GPT-6 Sol brings much of GPT-6 Astra down into a cheaper model supporting coding, computer use and agentic workflows. The notable differences lie not only in benchmarks, but in API price, context and how each company positions its product.

Quick comparison: Claude Opus 5.5 vs GPT-6 Sol

Anthropic lists Opus 5.5 at $4 input and $20 output per million tokens; OpenAI lists GPT-6 Sol at $2 and $10 respectively. So on standard per-token price, Sol is half of Opus 5.5 on both input and output. However, the cost to finish a real task also depends on token count, agent steps and tool calls — so you can't conclude which model runs cheaper on every workload from unit price alone.

Claude Opus 5.5: Anthropic centers on coding and long-running agents

Anthropic calls Opus 5.5 its strongest Opus model yet, focused on coding, professional work and agents that must run for a long time. One notable change is operating efficiency.

Anthropic says Opus 5.5 can match Claude Fable 5.1 on most work while costing about 40% less than Opus 5 on a typical workload. Listed token price is also 20% lower, and cache reads are 60% cheaper than Opus 5.

Anthropic also cites an early tester: the model completed a migration of a roughly 680,000-line codebase in under a day — work the tester estimated could take an engineering team weeks. This is Anthropic's own example, not an independent benchmark, but it shows the kind of workload the company wants Opus 5.5 to handle.

GPT-6 Sol: bringing part of GPT-6 down to a lower price

OpenAI now has three notable GPT-6 tiers:

• GPT-6 Astra → the strongest model, for the hardest end-to-end work.
• GPT-6 Sol → complex coding and agentic workflows, but more cost-balanced.
• GPT-6 Luna → focused, high-volume, price-sensitive tasks.

GPT-6 Sol and Luna were released on Sep 22. OpenAI says the two inherit many of Astra's improvements while optimizing caching and inference to cut cost. Sol has a 1.05M-token context window, 128,000 max output tokens and supports reasoning effort from none to max. Official tools include function calling, web search, file search and computer use.

That makes Sol especially notable for systems that need the chain: reasoning → search → read files → call tools → operate the computer → keep reasoning.

Coding: where the two models collide most directly

Both Anthropic and OpenAI put coding/agents at the center of this generation. Anthropic markets Opus 5.5 for tasks like large code migrations, debugging, autonomous coding and long-running agents. OpenAI describes GPT-6 Sol directly as a model 'built to power complex coding and agentic workflows'.

Early reports also note Sol improves coding and computer use over GPT-5.6 Sol. But Anthropic and OpenAI use different benchmarks, harnesses and reasoning setups, so a single benchmark number shouldn't declare which model 'codes better' in every case.

For developers, a more useful test may be to give both models the same repository and measure: task completion rate → number of fixes → tests passing → tokens → time → final cost.

Context: both step into the one-million-token zone

A difference that once mattered a lot between AI models is narrowing. GPT-6 Sol has 1.05M-token context. Anthropic also brought the Opus line to the 1M-token context zone, serving large workloads like codebases, long documents and agents that must hold lots of context while working.

For developers, a million tokens doesn't mean stuffing the whole repository into every prompt. Large context is most useful combined with retrieval + caching + tool use + context management. Otherwise cost and noise can still spike.

API price: GPT-6 Sol is clearly cheaper on the list

This is the easiest difference to see. Claude Opus 5.5: input $4 / cached input $0.20 / output $20 per million tokens. GPT-6 Sol: input $2 / cached input $0.20 / output $10 per million tokens.

For a workload of 10M input + 2M output (excluding cache and tool calls): standard token cost for Opus 5.5 ≈ 10 × 4 + 2 × 20 = $80; for GPT-6 Sol ≈ 10 × 2 + 2 × 10 = $40. In this example Sol's token cost is half.

But that isn't Total Cost of Task. If a model needs fewer agent loops, less output or uses cache better, the final gap can differ. Anthropic emphasizes exactly this when it says Opus 5.5 cuts about 40% of typical-workload cost versus Opus 5.

If you need dirt cheap, Opus 5.5's rival isn't Luna

GPT-6 Luna costs just $0.10 input / $0.01 cached input / $0.50 output per million tokens. But OpenAI positions Luna for focused, high-volume tasks, not the same product tier as Claude Opus 5.5. So comparing Opus 5.5 to Luna isn't really fair.

A production architecture might even use several model types, routing by task difficulty:

• Classification / Extraction → Luna (cheap, high volume).
• Coding / Agent → Sol.
• Hard, long-running task → Opus 5.5 / Astra.

This may be a more important trend than 'which model wins': companies route each task to the right model instead of using the strongest model for every request.

Claude Code vs Codex/ChatGPT Work is part of the competition too

Comparing API models alone isn't enough. Anthropic ties Opus 5.5 tightly to Claude Code, including a Fast Mode whose stated peak speed is more than 2.5× faster at $8 input and $40 output per million tokens. On OpenAI's side, GPT-6 Sol and Luna are already in Codex and ChatGPT Work, and available via API.

This shows the competition moving from 'Model vs Model' to 'Model + Agent + Tools + Workspace + Integrations'. A model with high benchmarks but a poor developer workflow may deliver less value than one a few points lower that integrates well with the repository, terminal, browser and CI/CD.

'ChatGPT 6' is not actually a single model

This is worth explaining to avoid misleading headlines. GPT-6 is now a family. OpenAI positions:

• Astra — the strongest model for hard end-to-end reasoning/coding.
• Sol — balances intelligence and cost, focused on coding/agents.
• Luna — most efficient for clear, high-volume workloads.

So if you ask 'Claude Opus 5.5 vs ChatGPT 6, which is stronger?', you first have to say whether you mean Astra, Sol or Luna, and what kind of work you're doing. Opus 5.5 vs Sol is reasonable for coding/agents at production cost; for top frontier capability, the more relevant pair is Claude Opus 5.5 vs GPT-6 Astra.

Frequently asked questions

• Where is Claude Opus 5.5 strong? — Coding, professional work and long-running agents; API price $4 input / $20 output per million tokens.
• How is GPT-6 Sol different? — Aimed at complex coding and agentic workflows, 1.05M-token context, $2 input / $10 output.
• Is GPT-6 Luna a rival to Opus? — Not directly; Luna is optimized for high-volume workloads at $0.10 / $0.50.
• What to compare for top capability? — GPT-6 Astra is OpenAI's flagship for the hardest tasks; that's the model to put next to Opus 5.5.
• How far to trust benchmarks? — Benchmarks alone can't say which model fits everyone; for production AI, measure task success + latency + token usage + tool calls + total cost on real workloads.

About this piece: a sourced summary by DETOON. Prices and specs are as announced by Anthropic and OpenAI at launch and may change.