Best LLM for coding
4 models share first place — joint 1st of the 24 models on the Coding board in the last 24 hours — from DeepSeek V4.1 Flash (96 ± 0.3) to GPT-6 Sol (95 ± 0.6), none of them measurably behind the leader.
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As of 26 Oct 2026, 11:22 UTC · Coding board, last 24 hours
- 24
- models ranked on the Coding board in the last 24 hours
- 4
- models share first place: joint 1st, none measurably behind the leader
- 4 h
- between re-tests: the coding suite runs every four hours
01
Which AI model is best for coding right now?
4 models share first place — joint 1st of the 24 models on the Coding board in the last 24 hours — from DeepSeek V4.1 Flash (96 ± 0.3) to GPT-6 Sol (95 ± 0.6), none of them measurably behind the leader.
The Coding board is the 24-hour view: the average of each model’s runs over the last day, ranked with statistical ties, so one odd attempt cannot move a place. The table shows the five best-placed models; the full board, with every model and the 7-day and 30-day views, is on the leaderboard.
| Place | Model | Vendor | Score | Trend |
|---|---|---|---|---|
| =1 | DeepSeek V4.1 Flash | DeepSeek | 96 ± 0.3 | holding steady |
| =1 | Claude Opus 5.5 | Anthropic | 95 ± 1.2 | holding steady |
| =1 | GPT-5.6 Luna | OpenAI | 95 ± 0.5 | holding steady |
| =1 | GPT-6 Sol | OpenAI | 95 ± 0.6 | holding steady |
| =5 | GPT-6 Astra | OpenAI | 94 ± 0.6 | holding steady |
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02
How the leader is decided
The Coding board: repository debugging tasks graded by running the project’s own tests, including hidden tests the model never sees. Every model gets the same tasks on the same schedule — coding every four hours, reasoning and tool use daily, with an hourly canary probe — and the board re-ranks after every run.
Every score carries a standard error from its own run-to-run repeatability. Walking down a board, a model joins the group above it unless that group’s leader is ahead by more than 1.96 times their combined standard error, so places such as 1, 1, 1, 4 are normal and a shared place is written joint. A model graded on fewer tasks than the rest keeps its position and is never called tied.
On the Coding board in the last 24 hours, 4 models share first place: the measurement cannot separate them, and the one listed first could be listed last after the next run without anything having changed.
The rule, with every weight, is on the methodology page.
03
What the coding test measures
Every four hours. Seven tasks, all run every sweep, seven attempts each, graded by execution.
- The task
- Most tasks hand the model a small working project and a bug report written as a user complaint — not a diagnosis, and no file named. The cause sits a module away from the symptom, with a plausible decoy in between. The model must find the defect and return a corrected file, which is graded by running the project’s own test suite.
- Hidden tests
- Tests the model never sees make up three quarters of each repository task’s score, so a fix that satisfies the visible tests and is still wrong does not pass.
- Seven attempts
- Seven attempts per task collapse to one outcome per task by median, so one unlucky attempt cannot move a task.
- Only the answer
- Only the answer is graded, never a model’s hidden reasoning. An attempt that uses its whole output budget without answering, or answers without code, is a failed attempt, and a retry asks the identical question.
Nine measures, by weight: correctness 60, stability 10, edge cases 10, debugging 10, code quality 5, format 3 and safety 2; efficiency and complexity are measured and shown but carry no weight.
| Measure | Weight | What it measures |
|---|---|---|
| Correctness | 60% | Does the fix work? Graded by running the project’s tests |
| Stability | 10% | The same result attempt after attempt |
| Edge cases | 10% | Hidden tests the model never saw |
| Debugging | 10% | Did it find the real defect, not silence the symptom? |
| Code quality | 5% | Clean, maintainable code |
| Format | 3% | Guardrail: clean, parseable output |
| Safety | 2% | Guardrail: no dangerous operations |
| Efficiency | 0% | Measured and shown; carries no weight |
| Complexity | 0% | Measured and shown; carries no weight |
The methodology is public — every weight, statistical test and drift constant. The test tasks are private, so no lab can train on them. The full method is on the methodology page.
04
Does the best coding model change week to week?
Sometimes, and the board is built to show when a change is real. A shared place is the board’s way of saying the measurement cannot separate those models, so the one listed first among them can be listed last after the next run without anything having changed; a model moves to a place of its own only when the gap is beyond the measurement noise. In the last 24 hours, 4 models share first place on the Coding board.
The boards re-rank after every run — coding every four hours, reasoning and tool use daily, with an hourly canary probe — and the 24-hour view averages the runs of the last day, so one odd attempt cannot move a place; the 7-day and 30-day views on the leaderboard smooth it further.
A model that slips for good is a different question: every suite has its own Page-Hinkley (a CUSUM-family test) change-point detector that compares each model with its own past, and the current answer, model by model, is on Is AI getting worse?. If the question is whether ChatGPT or Claude is getting worse, or a model you use has been nerfed, the family pages answer it with the same data (is ChatGPT getting worse, is Claude getting worse); the method is on AI drift detection.
Three models free · email when a watched model's coding score falls five points in a week
05
Is Claude better than GPT for coding?
On the Coding board in the last 24 hours, the best-placed Claude model is Claude Opus 5.5 (joint 1st of 24, 95 ± 1.2) and the best-placed GPT model is GPT-5.6 Luna (joint 1st of 24, 95 ± 0.5). They share a place, so neither family leads on coding: a statistical tie.
Both on one page: Claude Opus 5.5 vs GPT-5.6 Luna. Every Claude model is on Anthropic models and every GPT model on OpenAI models; the other four families — Google (Gemini), DeepSeek, Moonshot AI (Kimi) and Z.ai (GLM) — are on the same board above.
06
What about price?
The Coding board ranks ability, not price. What a model costs in use depends on its list price per million tokens and on how many attempts it needs: a model that writes four times as much, or fails and retries, costs more per correct answer than its list price suggests.
Among the models within five points of the best on the Coding board in the last 24 hours, the lowest cost per correct answer over the last 30 days is GPT-6 Luna at about $0.31 per 1,000 correct coding answers, with a pass rate of 92%. The ranking is on best value AI model, and the fastest of the same group, GPT-5.6 Luna at a median of 3.3 s, on fastest AI model.
The head-to-head pages put list price, cost per correct answer and response time beside the scores.
07
How do I route to it automatically?
One OpenAI-compatible endpoint sends each request to whichever model the boards favour for that kind of work, with your own provider keys.
Set model to auto-coding on the Smart Router’s OpenAI-compatible endpoint and each request goes to the model placing highest on the Coding board. auto-value-coding picks the most coding points per measured dollar among models within five points of the coding leader. The router ranks on the same benchmark results as this page and re-decides as they move; routing is drift-aware by default, uses your own provider keys and adds zero markup on tokens. Free includes 1,000 routed requests a month.
Every auto-* mode is described in the router documentation.
1,000 routed requests a month free · your own provider keys · no card
08
Best LLM for coding: questions people ask
Which LLM is best for coding in October 2026?
4 models share first place — joint 1st of the 24 models on the Coding board in the last 24 hours — from DeepSeek V4.1 Flash (96 ± 0.3) to GPT-6 Sol (95 ± 0.6), none of them measurably behind the leader. As of 26 Oct 2026, 11:22 UTC. The board re-ranks after every run, so the answer on this page follows the leaderboard.
Is Claude better than GPT for coding?
On the Coding board in the last 24 hours, the best-placed Claude model is Claude Opus 5.5 (joint 1st of 24, 95 ± 1.2) and the best-placed GPT model is GPT-5.6 Luna (joint 1st of 24, 95 ± 0.5). They share a place, so neither family leads on coding: a statistical tie. Both on one page: Claude Opus 5.5 vs GPT-5.6 Luna.
How is the best coding model decided?
The Coding board: repository debugging tasks graded by running the project’s own tests, including hidden tests the model never sees. Every score carries a standard error from its own run-to-run repeatability. Walking down a board, a model joins the group above it unless that group’s leader is ahead by more than 1.96 times their combined standard error, so places such as 1, 1, 1, 4 are normal and a shared place is written joint. A model graded on fewer tasks than the rest keeps its position and is never called tied. The board is the 24-hour view, the average of each model’s runs over the last day, and every model gets the same tasks on the same schedule: coding every four hours, reasoning and tool use daily, with an hourly canary probe. The rule is on the methodology page.
Which LLM is best for debugging?
The coding suite is a debugging test: most tasks hand the model a small working project and a bug report written as a user complaint, with the cause a module away from the symptom and a plausible decoy in between, and the fix is graded by running the project’s own tests, including hidden ones. Correctness carries 60% of the score and finding the real defect rather than silencing the symptom another 10%, so the Coding board is the debugging ranking. 4 models share first place — joint 1st of the 24 models on the Coding board in the last 24 hours — from DeepSeek V4.1 Flash (96 ± 0.3) to GPT-6 Sol (95 ± 0.6), none of them measurably behind the leader.
How often does the ranking update?
Every model on the board runs the same tasks on the same schedule: coding every four hours, reasoning and tool use daily, with an hourly canary probe. This page reads the 24-hour board and refreshes every fifteen minutes. The methodology is public — every weight, statistical test and drift constant. The test tasks are private, so no lab can train on them.
Can I route to the best coding model automatically?
Set model to auto-coding on the Smart Router’s OpenAI-compatible endpoint and each request goes to the model placing highest on the Coding board. auto-value-coding picks the most coding points per measured dollar among models within five points of the coding leader. The router ranks on the same benchmark results as this page and re-decides as they move; routing is drift-aware by default, uses your own provider keys and adds zero markup on tokens. Free includes 1,000 routed requests a month. Every auto-* mode is described in the router documentation.
Route to the best automatically
Free account: every ranking over 24 hours, 7 days and 30 days, a 3-model watchlist with email alerts, 1,000 routed requests a month and a Data API key. No card.
model: auto-coding · your own provider keys · zero markup on tokens
Free account · 24 hours, 7 days and 30 days · no card
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