GPT-5.1-Codex-Mini vs Qwen3 235B A22B Instruct 2507
GPT-5.1-Codex-Mini leads Qwen3 235B A22B Instruct 2507 on the Artificial Analysis Intelligence Index, 20.4 to 12.0, and Qwen3 235B A22B Instruct 2507 costs 2.7x less per output token ($0.75 vs $2.00 per million). Updated October 22, 2026; 17 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Qwen3 235B A22B Instruct 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for a larger context window (400K against 262K tokens) and work measured by MMLU Pro (Artificial Analysis run) or Design Arena: 3D, where it leads.
- Shared results
- 17
- GPT-5.1-Codex-Mini only
- 5
- Qwen3 235B A22B Instruct 2507 only
- 7
- Like-for-like categories
- 8 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
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 12.0.
Same board, both measured
Web design
Design Arena: websites
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 1111 against 1058.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Instruct 2507
GPT-5.1-Codex-Mini $0.0013, Qwen3 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- Coding, 11.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- Terminal-Bench Hard (Artificial Analysis run): GPT-5.1-Codex-Mini 33.3% against Qwen3 235B A22B Instruct 2507 15.2%, 18.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Qwen3 235B A22B Instruct 2507 $0.75 per million output tokens, 2.7x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Qwen3 235B A22B Instruct 2507 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-Mini | Bars | Qwen3 235B A22B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| Web design | 5 | 1 | ||
| General | 1 | 1 | ||
| All shared boards | 14 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | 78.8% | GPT-5.1-Codex-Mini |
| SciCode | Not measured | 41.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 15.2% | GPT-5.1-Codex-Mini |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| WeirdML | Not measured | 38.7% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena WebDev: HTML | 1253 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 12.0 | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 53.2% | GPT-5.1-Codex-Mini |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 52.1% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 79.0% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 15.9% | GPT-5.1-Codex-Mini |
| ARC-AGI | Not measured | 11.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | Not measured | 1.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 18 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | 91.0 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 91.7% | 91.0% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | 84.3% | Qwen3 235B A22B Instruct 2507 |
| Neither model has a result on the other 17 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | 1058 | GPT-5.1-Codex-Mini |
| Design Arena: UI components | 1078 | 962 | GPT-5.1-Codex-Mini |
| Design Arena: game dev | 1092 | 951 | GPT-5.1-Codex-Mini |
| Design Arena: data viz | 1097 | 1070 | GPT-5.1-Codex-Mini |
| Design Arena: 3D | 988 | 994 | Qwen3 235B A22B Instruct 2507 |
| Design Arena: all code categories | 1093 | 1038 | GPT-5.1-Codex-Mini |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | 67.9% | 51.2% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 66.7% | 72.0% | Qwen3 235B A22B Instruct 2507 |
| DTBench | Not measured | 78.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMCA | Not measured | 23.5% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Qwen3 235B A22B Instruct 2507 | 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
Qwen3 235B A22B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B Instruct 2507 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-Mini | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | July 21, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.25 | $0.15 |
| Output $/Mtok | $2.00 | $0.75 |
| 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-Mini better than Qwen3 235B A22B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for a larger context window (400K against 262K tokens) and work measured by MMLU Pro (Artificial Analysis run) or Design Arena: 3D, where it leads.
Which is cheaper, GPT-5.1-Codex-Mini or Qwen3 235B A22B Instruct 2507?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Qwen3 235B A22B Instruct 2507 costs $0.15 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Qwen3 235B A22B Instruct 2507?
GPT-5.1-Codex-Mini, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Qwen3 235B A22B Instruct 2507 comparison based on?
17 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Qwen3 235B A22B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.