Gemini 2.5 Pro Preview 06-05 vs GPT-5.1-Codex-Max
Updated October 21, 2026. 2 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Gemini 2.5 Pro Preview 06-05 has the edge where the context window is larger, 1.05M against 400K tokens, and where what matters is LiveCodeBench (Vals AI) or METR time horizons, where it leads.
- Shared results
- 2
- Gemini 2.5 Pro Preview 06-05 only
- 12
- GPT-5.1-Codex-Max only
- 4
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Long documents
Listed context window
Gemini 2.5 Pro Preview 06-05
Gemini 2.5 Pro Preview 06-05 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Gemini 2.5 Pro Preview 06-05 $0.0063, GPT-5.1-Codex-Max $0.0063 per turn.
List prices
At a glance
- Coding index
- Gemini 2.5 Pro Preview 06-05 not benchmarked, GPT-5.1-Codex-Max not benchmarked.
- Widest category
- Agentic work, 15.4 points apart on average over 1 shared benchmark.
- Widest single gap
- METR time horizons: Gemini 2.5 Pro Preview 06-05 55.4% against GPT-5.1-Codex-Max 70.8%, 15.4 points apart on a 0 to 100 scale.
- Output price
- Both cost $10.00 per million output tokens.
- Context window
- Gemini 2.5 Pro Preview 06-05 takes 1.05M tokens, GPT-5.1-Codex-Max 400K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Gemini 2.5 Pro Preview 06-05 | Bars | GPT-5.1-Codex-Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Aider polyglot | 83.1% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| LiveCodeBench (Vals AI) | 79.2% | 83.6% | GPT-5.1-Codex-Max |
| Vibe Code Bench (Vals AI) | Not measured | 22.2% | Only GPT-5.1-Codex-Max measured |
| LiveCodeBench (Artificial Analysis run) | 77.8% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| Terminal-Bench | Not measured | 60.4% | Only GPT-5.1-Codex-Max measured |
| GSO-Bench | 3.9% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| ALE-Bench | Not measured | 1228.3 | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| METR time horizons | 55.4% | 70.8% | GPT-5.1-Codex-Max |
| Remote Labor Index | 0.8% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| DeepResearch Bench | 42.8% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 83.6% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| Humanity's Last Exam (Artificial Analysis run) | 19.9% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| GPQA Diamond (Epoch AI run) | 84.8% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| SimpleBench | 62.4% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| VPCT | 46.4% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| ProofBench | Not measured | 9.0% | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.8% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Fiction.LiveBench | 91.7% | Not measured | Only Gemini 2.5 Pro Preview 06-05 measured |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro Preview 06-05 | GPT-5.1-Codex-Max | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Same cost
Repository review
50K input + 3K output tokens
Same cost
Agent loop
200K input + 10K output tokens
Same cost
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Google Gemini 2.5 Pro Preview 06-05 | OpenAI GPT-5.1-Codex-Max |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | June 5, 2025 | December 4, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.25 | $1.25 |
| Output $/Mtok | $10.00 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro Preview 06-05 better than GPT-5.1-Codex-Max for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Gemini 2.5 Pro Preview 06-05 has the edge where the context window is larger, 1.05M against 400K tokens, and where what matters is LiveCodeBench (Vals AI) or METR time horizons, where it leads.
Which is cheaper, Gemini 2.5 Pro Preview 06-05 or GPT-5.1-Codex-Max?
Gemini 2.5 Pro Preview 06-05 costs $1.25 per million input tokens and $10.00 per million output tokens; GPT-5.1-Codex-Max costs $1.25 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemini 2.5 Pro Preview 06-05 or GPT-5.1-Codex-Max?
Gemini 2.5 Pro Preview 06-05, with 1.05M tokens against 400K.
How many benchmarks is the Gemini 2.5 Pro Preview 06-05 vs GPT-5.1-Codex-Max comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro Preview 06-05's coding index rests on 0 independent benchmarks and GPT-5.1-Codex-Max's on 1. Where only one model has a result, the page says so instead of naming a winner.