GPT-5.1-Codex-Max vs Kimi K2 0905
Kimi K2 0905 costs 4.0x less per output token than GPT-5.1-Codex-Max ($2.50 vs $10.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
GPT-5.1-Codex-Max or Kimi K2 0905: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens).
- Shared results
- 1
- GPT-5.1-Codex-Max only
- 5
- Kimi K2 0905 only
- 15
- Like-for-like categories
- 1 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
Intelligence
AA Intelligence Index
No call
Only Kimi K2 0905 has a AA Intelligence Index result, 15.3.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1089.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2 0905
GPT-5.1-Codex-Max $0.0063, Kimi K2 0905 $0.0019 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Kimi K2 0905 not benchmarked.
- Widest category
- Coding, 32.0 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-5.1-Codex-Max 1228.3 against Kimi K2 0905 267.1, 32.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Max $10.00 against Kimi K2 0905 $2.50 per million output tokens, 4.0x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Kimi K2 0905 262K.
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 | GPT-5.1-Codex-Max | Bars | Kimi K2 0905 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | Not measured | Only GPT-5.1-Codex-Max measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 61.0% | Only Kimi K2 0905 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only Kimi K2 0905 measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| WeirdML | Not measured | 36.7% | Only Kimi K2 0905 measured |
| ALE-Bench | 1228.3 | 267.1 | GPT-5.1-Codex-Max |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 15.3 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 73.4% | Only Kimi K2 0905 measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.7% | Only Kimi K2 0905 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 6.4% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 57.3 | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 57.3% | Only Kimi K2 0905 measured |
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 81.9% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1089 | Only Kimi K2 0905 measured |
| Design Arena: all code categories | Not measured | 1098 | Only Kimi K2 0905 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | Leader |
|---|---|---|---|
| IFBench | Not measured | 41.7% | Only Kimi K2 0905 measured |
| Long context reasoning (AA-LCR) | Not measured | 53.0% | Only Kimi K2 0905 measured |
| Fiction.LiveBench | Not measured | 66.7% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Kimi K2 0905 | 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
Kimi K2 0905 costs less
Repository review
50K input + 3K output tokens
Kimi K2 0905 costs less
Agent loop
200K input + 10K output tokens
Kimi K2 0905 costs less
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 | OpenAI GPT-5.1-Codex-Max | MoonshotAI Kimi K2 0905 |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | September 4, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.60 |
| Output $/Mtok | $10.00 | $2.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Kimi K2 0905 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5.1-Codex-Max or Kimi K2 0905?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Kimi K2 0905 costs $0.60 and $2.50. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Kimi K2 0905?
GPT-5.1-Codex-Max, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Max vs Kimi K2 0905 comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Kimi K2 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.