The best LLM for coding is the one whose builds you would keep, at a price you would pay. We gave the four strongest coding models of late 2026 the same prompts, word for word, one attempt each, nothing edited afterwards. The live table below aggregates every build. Underneath it is my recommendation, and the builds to argue with it.
| Model | Briefs finished | Median wall time | Output tokens, all briefs | Average API-equivalent cost per build | Runtime errors | Interrupted attempts |
|---|---|---|---|---|---|---|
| Claude Opus 5.5 | 7 of 8 | 1h 14m | 1.8M (+1 not recorded) | $14.53 | 0 | 5 |
| Claude Fable 5.1 | 8 of 8 | 36m 57s | 1.3M | $13.85 | 0 | 5 |
| GPT-6 Astra | 8 of 8 | 11m 29s | 340k | $3.90 | 0 | 0 |
| GPT-6.1 Sol | 8 of 8 | 12m 05s | 394k | $0.80 | 0 | 0 |
Which LLM should you use for coding?
Default to Claude Opus 5.5; switch to GPT-6.1 Sol when cost or speed matters more than polish.
- Claude Opus 5.5 built the richest results on every brief and tested its own work most. It is also the cheapest Claude per token, $4 and $20 per million, and Anthropic's own model guide recommends it for most workloads.
- GPT-6.1 Sol is the value pick: every build finished in under 20 minutes for $0.54 to $1.12 at list prices, per OpenAI's published rates.
- Claude Fable 5.1 builds calmer, more restrained work at $10 and $50 per million. It never beat Opus by enough on our briefs to justify the higher rate.
- GPT-6 Astra costs five times Sol per token and, on our briefs, produced work strikingly close to Sol's.
How do the four LLMs differ on real builds?
In how long they work, and therefore in what they deliver. On the 3D shooter, Opus 5.5 was still working when our 150-minute limit arrived and its files were published as they stood; Fable finished in 75 minutes, Astra in 20 and Sol in 13. Two of the four shooters end in a game over before our 40-second input script is done.
Does the bigger context window matter for coding?
Not between these four. Both Claude models list 1 million tokens of context and both GPT models list 1.05 million, all with 128,000 output tokens, per Anthropic's and OpenAI's model pages. None of our briefs came close to those limits. What separated the models was how they spent their tokens: the Claude runs re-read their projects on every step, which is why Opus read about 10 million input tokens to build one landing page.
How do you check this for your own code?
Copy one of our prompts from a brief page, run it on the model you are considering, and compare its build with ours. The commands we used are on the methodology page, so the comparison is fair. For the same comparison as a single chart with release dates and prices, see the AI model comparison chart.
Four physics simulations written from scratch, names hidden. Pick the one you trust.
Take the blind test
Frequently asked questions
Which LLM is best for coding in 2026?
Claude Opus 5.5 produced the most complete builds on our briefs and is Anthropic's recommended default. GPT-6.1 Sol is the best value: complete, working builds in under 20 minutes at about a dollar each. Claude Fable 5.1 and GPT-6 Astra are premium options that did not justify their price on these briefs.
Is GPT-6 or Claude better at coding?
They are better at different things. The Claude models tested and refined their work for 25 to 150 minutes and shipped more elaborate builds. The GPT-6 models finished in 7 to 25 minutes with clean, working, simpler results. The builds are side by side so you can judge the difference.
Why not just use a coding benchmark?
Benchmarks score narrow tasks such as fixing a known bug. They do not show whether a model's dashboard looks designed or whether its game is fun. We publish measurements and the builds themselves, so you can see the quality a score leaves out.
Which LLM is cheapest for coding?
GPT-6.1 Sol, at $2 per million input tokens and $10 per million output tokens. Its builds cost $0.54 to $1.12 at list prices, against $2.12 to $7.27 for GPT-6 Astra and about $7 to $25 for the Claude models.