GPT-6.1 Sol leads on coding; GPT-6.1 Sol is cheaper per million tokens.
1,000 routed requests a month free · your own provider keys · no card
Free account · 24 hours, 7 days and 30 days · no card
Already have an account? Log in
As of 26 Oct 2026, 11:37 UTC · 24-hour board
01
Both models on the same four boards, the same list-price view and the same drift detectors. Scores carry their standard error; a place is shared, written joint, when the gap is inside that noise.
| Measure | Gemini 4 Argon | GPT-6.1 Sol |
|---|---|---|
| Combinedscore ± standard error · place, 24-hour board | 65 ± 2.024th of 24 | 81 ± 2.3joint 16th of 24 |
| Codingscore ± standard error · place, 24-hour board | 65 ± 2.524th of 24 | 93 ± 0.8joint 5th of 24 |
| List price per 1M tokensinput / output · blended 40/60 | $4 / $20$13.60 blended | $2 / $10$6.80 blended |
| Median response timecoding tasks, last 30 days | 9.2 s | 13 s |
| Pass ratecoding attempts, last 30 days | 60% | 91% |
| Cost per 1,000 correct answerscoding, last 30 days, every attempt billed | $17.50 | $6.65 |
| Drift statustrend on the 24-hour board · Page-Hinkley per suite | Holding steady | Holding steadyno detection on coding, reasoning and tool use |
| Tracked since | 13 Oct 2026 | 29 Sep 202612 days |
The boards re-rank after every run, so the figures here follow the leaderboard: each model’s own page, Gemini 4 Argon and GPT-6.1 Sol, carry the full history. The whole board for each category is on best LLM for coding, best LLM for reasoning and best AI model for agents.
02
The Coding board: seven repository debugging tasks graded by running the project’s own tests, including hidden tests the model never sees, re-tested every four hours.
Gemini 4 Argon scores 65 ± 2.5 — 24th of the 24 models on the Coding board in the last 24 hours. GPT-6.1 Sol scores 93 ± 0.8 — joint 5th of the 24 models on the Coding board in the last 24 hours. The gap of 28 points is beyond the measurement noise, so GPT-6.1 Sol leads on coding.
Over the last 30 days, Gemini 4 Argon passed 60% of its coding attempts and GPT-6.1 Sol 91%.
See every model on the Coding board on best LLM for coding, or each model’s own page, Gemini 4 Argon and GPT-6.1 Sol.
03
The Reasoning board: four long, multi-turn working sessions every day, with continuity checked by running code against rules stated earlier in the session.
GPT-6.1 Sol scores 58 ± 2.5 — joint 17th of the 22 models on the Reasoning board in the last 24 hours.
See every model on the Reasoning board on best LLM for reasoning.
04
The Tool use board: nine tasks, each in a real sandboxed machine — either the job is done at the end or it is not. Task completion, tool selection, parameter accuracy, efficiency, error handling, context awareness and safety are scored.
GPT-6.1 Sol scores 81 ± 2.5 — joint 14th of the 22 models on the Tool use board in the last 24 hours.
See every model on the Tool use board on best AI model for agents.
05
List prices are each provider’s standard rate per million tokens, cache-miss input and output. Cost per correct answer counts every attempt on the coding tasks, failed ones included, over the last 30 days.
Gemini 4 Argon lists at $4 in / $20 out per 1M tokens; GPT-6.1 Sol at $2 in / $10 out.
On a 40/60 input–output blend, Gemini 4 Argon costs about 2× as much per token as GPT-6.1 Sol.
Over the last 30 days, 1,000 correct coding answers cost about $17.50 with Gemini 4 Argon and $6.65 with GPT-6.1 Sol, every attempt billed, failed ones included.
The cheapest model that still passes the tests is on best value AI model.
06
Median response time of one attempt or one session, from the same runs as the scores, over the last 30 days.
Median response time on the coding tasks over the last 30 days: Gemini 4 Argon 9.2 s, GPT-6.1 Sol 13 s.
Gemini 4 Argon answers about 1.4× faster.
Every model’s median is on fastest AI model.
07
Page-Hinkley change-point detection (a CUSUM-family test) runs per suite on each model’s daily median, with every constant published; a real shift is confirmed over days, not hours. An hourly canary of two fixed coding probes catches sharp drops.
Gemini 4 Argon is holding steady on the 24-hour board.
GPT-6.1 Sol is holding steady on the 24-hour board; no Page-Hinkley detection on coding, reasoning or tool use.
The family pages answer the question for every model of a provider: Is Gemini getting worse? and Is ChatGPT 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
Three models free · email when a watched model's coding score falls five points in a week
08
Only a difference that clears a threshold counts: a board lead beyond the measurement noise (the leaderboard’s own tie rule), 10% on list price, 20% on response time or cost per correct answer.
09
One OpenAI-compatible endpoint sends each request to whichever model the boards favour for that kind of work, with your own provider keys.
Yes. The Smart Router is one OpenAI-compatible endpoint with 14 auto-* routing modes: a quality mode such as auto-coding sends each request to the model placing highest on that board, the value modes pick within 5 points of the best and then the cheapest measured cost per run, and auto-split sends traffic by weights you set. 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 mode is described in the router documentation.
1,000 routed requests a month free · your own provider keys · no card
10
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.
Scores come from our own runs through each provider’s public API, not vendor-reported numbers. Every model gets the same prompts and output budget, and the same sampling settings wherever a provider accepts them, and is graded by executing what it produces. Every score carries a standard error from its own run-to-run repeatability; models whose lead is inside that noise share a place, written joint.
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, weights and constants are on the methodology page.
11
Gemini 4 Argon scores 65 ± 2.5 — 24th of the 24 models on the Coding board in the last 24 hours. GPT-6.1 Sol scores 93 ± 0.8 — joint 5th of the 24 models on the Coding board in the last 24 hours. The gap of 28 points is beyond the measurement noise, so GPT-6.1 Sol leads on coding. Over the last 30 days, Gemini 4 Argon passed 60% of its coding attempts and GPT-6.1 Sol 91%.
GPT-6.1 Sol scores 58 ± 2.5 — joint 17th of the 22 models on the Reasoning board in the last 24 hours.
GPT-6.1 Sol scores 81 ± 2.5 — joint 14th of the 22 models on the Tool use board in the last 24 hours.
Gemini 4 Argon lists at $4 in / $20 out per 1M tokens; GPT-6.1 Sol at $2 in / $10 out. On a 40/60 input–output blend, Gemini 4 Argon costs about 2× as much per token as GPT-6.1 Sol. Over the last 30 days, 1,000 correct coding answers cost about $17.50 with Gemini 4 Argon and $6.65 with GPT-6.1 Sol, every attempt billed, failed ones included.
Median response time on the coding tasks over the last 30 days: Gemini 4 Argon 9.2 s, GPT-6.1 Sol 13 s. Gemini 4 Argon answers about 1.4× faster.
Gemini 4 Argon is holding steady on the 24-hour board. GPT-6.1 Sol is holding steady on the 24-hour board; no Page-Hinkley detection on coding, reasoning or tool use. Page-Hinkley change-point detection (a CUSUM-family test) runs per suite on each model’s daily median, with every constant published; a real shift is confirmed over days, not hours. An hourly canary of two fixed coding probes catches sharp drops.
GPT-6.1 Sol leads on coding; GPT-6.1 Sol is cheaper per million tokens. Choose Gemini 4 Argon when response time matters: a median of 9.2 s on the coding tasks against 13 s over the last 30 days. Choose GPT-6.1 Sol when the work is coding: it leads the Coding board by 28 points in the last 24 hours (93 ± 0.8 against 65 ± 2.5); price per token matters: it lists at $2 in / $10 out per 1M tokens against $4 in / $20 out; cost per correct answer matters: about $6.65 per 1,000 correct coding answers against $17.50 over the last 30 days.
Yes. The Smart Router is one OpenAI-compatible endpoint with 14 auto-* routing modes: a quality mode such as auto-coding sends each request to the model placing highest on that board, the value modes pick within 5 points of the best and then the cheapest measured cost per run, and auto-split sends traffic by weights you set. 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.
The Smart Router sends each request to whichever of Gemini 4 Argon and GPT-6.1 Sol — or any model on the board — the boards favour for that kind of work, with your own provider keys and zero markup on tokens.
1,000 routed requests a month free · your own provider keys · no card
Free account · 24 hours, 7 days and 30 days · no card
Already have an account? Log in