Comparison · verified August 21, 2026
Claude vs GPT-5.6 for Coding
Both families are close enough on coding that the model is rarely what decides your outcome. Published benchmarks split — Opus 5 leads on repository-level fixes and abstract reasoning, Sol leads on long-horizon task completion — and neither vendor runs the other’s harness.
So this page does something more useful than declaring a winner. It gives you the verified specs, labels every performance claim with who made it, names the billing details that move your bill more than model choice does, and shows you how to run the only comparison that settles the question for your codebase.
The verified differences
What each one costs
Last verified August 21, 2026
USD per million tokens, standard rates, no discounts applied. Read the three caveats underneath before you put these numbers in a spreadsheet.
| Model | Tier | Input | Output | Notes |
|---|---|---|---|---|
| Claude Fable 5 | frontier | $10 | $50 | The frontier model. Twice the price of Opus 5 for the hardest work. |
| Claude Opus 5 | frontier | $5 | $25 | The default flagship. Thinking on by default. Fast mode available at $10/$50. |
| Claude Sonnet 5 | balanced | $2 | $10 | What most production agent fleets run on. The September 2026 increase to $3/$15 was cancelled. |
| Claude Haiku 4.5 | fast | $1 | $5 | High-volume and latency-sensitive work. |
| GPT-5.6 Sol | frontier | $5 | $30 | The flagship tier. Long-context requests meter at $10/$45. |
| GPT-5.6 Terra | balanced | $2 | $12 | The everyday tier, cut to this rate on July 30, 2026. Long-context meters at $4/$18. |
| GPT-5.6 Luna | fast | $0.20 | $1.2 | The cheapest frontier-family option on the market. Long-context meters at $0.40/$1.80. |
Anthropic changed tokenizers at Claude 4.7
Claude 4.7 and later — including Opus 5, Sonnet 5, and Fable 5 — use a newer tokenizer that produces roughly 30% more tokens for the same text than Sonnet 4.6 and earlier. A $2/MTok model on the new tokenizer is not directly comparable to a $2/MTok model on an older one, or to another vendor. Compare cost per task, not cost per token.
OpenAI meters long context separately
GPT-5.6 publishes a second, higher rate for long-context requests: Sol goes from $5/$30 to $10/$45, Terra from $2/$12 to $4/$18, Luna from $0.20/$1.20 to $0.40/$1.80. Anthropic includes the full 1M-token window at standard pricing on Claude 4.6 and later — a 900k-token request costs the same per token as a 9k one. If your workload is context-heavy, that difference is larger than the headline gap.
Two of Google’s current rates are introductory
Gemini 3.7 Flash and 3.6 Flash are priced at $0.75/$3.75 only through December 31, 2026. Both double to $1.50/$7.50 in 2027. If you are building a twelve-month cost model on Gemini Flash, model the 2027 number, not the one on the page today.
Caching and batching move the number more than model choice
A Claude cache hit costs 10% of the standard input price, and the Batch API takes 50% off both directions; the two stack. A workload that reuses a large system prompt can land below a nominally cheaper model that you are calling uncached. Model selection is usually the third-largest lever, after caching and after not sending the context at all.
What the evidence says, and who is saying it
Almost every performance number in public circulation traces back to a vendor running its own harness. That does not make the numbers useless, but the attribution belongs next to the claim.
Anthropic, Claude Opus 5 announcement (July 24, 2026)
- Frontier-Bench v0.1 — surpasses all other models, and more than doubles Opus 4.8.
- CursorBench 3.2 — within 0.5% of Fable 5’s peak score at half the cost per task.
- ARC-AGI 3 — roughly three times the next-best model.
- OSWorld 2.0 — beats Fable 5’s best result at just over a third of the cost.
- Zapier AutomationBench — 1.5× the next-best model at the same cost.
Anthropic publishes these as relative claims rather than absolute percentages, and the announcement contains no GPT-5.6 comparison at all. "Next-best model" is doing unspecified work in several of them.
Third-party comparisons drawing on both vendors’ system cards
- SWE-bench Pro — Opus 5 reported well ahead of GPT-5.6 Sol on repository-level bug fixing.
- DeepSWE v1.1 — GPT-5.6 Sol reported ahead of Opus 5 by a few points.
- Sol reported stronger on long-horizon task completion and terminal coding speed.
These figures come from secondary write-ups citing each vendor’s own system card. We have not independently verified them, and the two vendors do not run identical harnesses. Treat the direction as informative and the magnitude as unverified.
Run the comparison that actually decides it
A benchmark measures performance on someone else’s repository. Yours has its own conventions, its own dependency graph, and its own particular way of being confusing. An afternoon of measurement beats a month of reading comparisons.
- Pull 20 real tasks from your own history. Closed bug tickets with a known correct fix are ideal, because the answer already exists and you did not write it for the test.
- Fix the harness and vary only the model. Same prompt, same context, same tools. Most published comparisons fail here, which is a large part of why they disagree.
- Score on your bar, not a leaderboard’s. Did it pass review? Did it need a second round? Would you have merged it?
- Record cost per completed task, not per token. With the tokenizer difference and the long-context meter, per-token price tells you very little about the bill.
- Re-run it when either vendor ships. Both families moved three times in the first eight months of 2026. A result from March is a historical note.
How to evaluate your agents covers the test-set design, and auditing your eval suite covers what to do when the suite stops catching things.
The honest bottom line
Pick Claude if your work is repository-scale — large refactors, reasoning across many files, long agentic sessions where a 1M-token window at flat pricing changes what is affordable. Claude Code is the more developed terminal agent, and Opus 5’s May 2026 knowledge cutoff is the most recent of any model here, which shows up on recent framework versions.
Pick GPT-5.6 if you are already inside the OpenAI ecosystem, if Luna’s $0.20 / $1.20 makes a high-volume classification or triage workload viable that nothing else does, or if your work is long-horizon single-track rather than broad across a repository.
Use both if you are running anything at scale. Routing cheap classification to Luna or Haiku 4.5 and hard reasoning to Opus 5 or Sol costs less than standardising on one flagship, and the two APIs are similar enough that the abstraction is about a day of work. Vendor lock-in on a model that may be retired within a year is a risk you can decline — three Claude models were retired or suspended in the first half of 2026 alone.