Short answer: Claude Fable 5.1 is the best coding model on published benchmarks, but GPT-6.1 Sol is close enough for most agentic work at one-tenth the price. Gemini 3.6 Flash is the value pick for high-volume code explanation, tests and review.
This page uses published scores, not vendor adjectives, and states clearly where a model has no public numbers.
For the full three-vendor comparison, see the AI model comparison for 2026.
Published benchmark scores
| Benchmark | Claude Fable 5.1 | Claude Opus 5 | Claude Fable 5 | GPT-5.6 Sol |
|---|---|---|---|---|
| Terminal-Bench 4.0 | 55.8% | 52.3% | 42.0% | 37.3% |
| Terminal-Bench-Science 0.1 | 52.6% | 29.0% | 24.7% | 22.4% |
| CursorBench 3.2.0 | 73.4% | 70.0% | 70.5% | 67.2% |
| AutomationBench | 31.4% | 26.9% | 17.1% | 19.6% |
Source: Anthropic’s Fable 5.1 release, September 2026. Fable 5.1 scored 60.9% on Terminal-Bench 4.0 in its Mythos configuration.
Important: OpenAI has not published GPT-6.1 scores on these benchmarks, and Google has not published Gemini 3.6 Flash scores. Their placement below rests on positioning, price and older benchmark results, not on a like-for-like comparison. Treat that as a gap, not a verdict.
The older SWE-Bench Pro figures from June 2026 put Claude Fable 5 at 80.3%, GPT-5.6 Sol at about 58% and Gemini 3.5 Flash at 55.1%. Those are a generation old and not directly comparable to the table above.
Cost per task
Benchmark percentage is not a budget. Take a coding workload of 2 million input tokens and 200,000 output tokens a month:
| Model | Monthly cost |
|---|---|
| Claude Fable 5.1 | 2 × $10 + 0.2 × $50 = $30 |
| GPT-6.1 Sol, direct | 2 × $1 + 0.2 × $5 = $3 |
| Gemini 3.6 Flash | 2 × $0.75 + 0.2 × $3.75 = $2.25 |
Fable 5.1 costs about ten times GPT-6.1 Sol and more than thirteen times Gemini 3.6 Flash for the same raw volume. The question is whether it saves more than that in rework.
For agentic coding, it often does. Fable 5.1 finishes tasks in fewer turns, which reduces the total token count, and it is less likely to produce a plausible but wrong patch that a developer has to unwind. For a team spending ₹1 lakh a month on engineering time per developer, a 10% reduction in rework dwarfs the token bill.
What the benchmarks do and do not measure
They measure:
- Whether the model can complete a task in a terminal or editor harness.
- Multi-step tool use under time and step limits.
- Some real-world repository work.
They do not measure:
- Whether the code fits your architecture and conventions.
- Review quality on a diff you already wrote.
- How the model behaves with your private libraries and internal APIs.
- Long-term maintenance of the code it produces.
A model can lead a benchmark and still be the wrong choice for a codebase with unusual patterns.
Recommendation by scenario
| Scenario | Best pick | Why |
|---|---|---|
| Complex refactor or migration | Claude Fable 5.1 | Highest published agentic coding scores |
| Agentic coding on a budget | GPT-6.1 Sol | Strong positioning at $1 / $5 direct |
| High-volume tests and code explanation | Gemini 3.6 Flash | Cheapest per token, fast |
| Computer-use coding agent | GPT-6.1 Terra | Precise computer use at $0.50 / $2.50 |
| Self-hosted or offline | Nemotron 3 Ultra or GLM 5.3 | No vendor dependency |
| Mixed writing and coding | Claude Fable 5.1 | Best prose control alongside code quality |
How to test on your own repository
- Pick 20 real tasks from your backlog, including two that failed before.
- Run each model in the same harness with the same tools.
- Score blind: correct, correct with edits, wrong, or dangerously wrong.
- Record turns and total tokens per task, not just per-token price.
- Measure cost per accepted change, then multiply by your monthly volume.
- Re-run the set after every model release; the ranking moves.
Common mistakes
- Choosing the benchmark leader without testing your own stack.
- Choosing the cheapest model and paying for it in review time.
- Comparing per-token prices across providers without checking marketplace markups.
- Ignoring batch and cache pricing, which can halve a real bill.
- Forgetting that a coding model also needs a good harness, tools and context.
FAQ
What is the best AI model for coding in 2026?
On published benchmarks, Claude Fable 5.1 leads Terminal-Bench 4.0, CursorBench 3.2.0 and AutomationBench. GPT-6.1 Sol is the better value if your tasks are not at the hardest end.
Is Gemini 3.6 Flash good for coding?
For code explanation, test generation, small refactors and review assistance, yes. It is not the model to choose for the hardest repository-scale work.
Do these benchmarks predict performance on my codebase?
No. They measure general capability in a harness. Your libraries, conventions and review standards change the ranking.
Are open-weight models good enough for coding?
For many routine tasks, yes. Nemotron 3 Ultra and GLM 5.3 are viable, especially when data cannot leave your infrastructure. Test them on the same tasks before assuming a closed model is required.
Next step
If you are choosing a coding model for a product or an internal team, our AI app development cost guide covers the build side. For a short evaluation on your own repository, contact 24Bit System.
Related reading: GPT-6.1 vs Claude Fable 5.1 and AI model benchmarks explained.
