GPT-5.1-Codex-Mini vs Qwen3.7 Flash
Qwen3.7 Flash costs 15.4x less per output token than GPT-5.1-Codex-Mini ($0.13 vs $2.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 20.4
- Output $/M
- $2.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.13
GPT-5.1-Codex-Mini or Qwen3.7 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-5.1-Codex-Mini has results on LMArena WebDev, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Qwen3.7 Flash on ALE-Bench, Epoch Capabilities Index and GPQA Diamond (Epoch AI run). Compare them on price and context instead.
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 GPT-5.1-Codex-Mini has a AA Intelligence Index result, 20.4.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
Long documents
Listed context window
Qwen3.7 Flash
Qwen3.7 Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Flash
GPT-5.1-Codex-Mini $0.0013, Qwen3.7 Flash $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Qwen3.7 Flash not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Qwen3.7 Flash $0.13 per million output tokens, 15.4x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Qwen3.7 Flash 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| ALE-Bench | Not measured | 420.1 | Only Qwen3.7 Flash 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 | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Epoch Capabilities Index | Not measured | 144.1 | Only Qwen3.7 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | Not measured | Only GPT-5.1-Codex-Mini measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.7 Flash measured |
| Mystery game puzzles | Not measured | 15.0% | Only Qwen3.7 Flash measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | Not measured | Only GPT-5.1-Codex-Mini measured |
| AIME 2025 (Artificial Analysis run) | 91.7% | Not measured | Only GPT-5.1-Codex-Mini measured |
| OTIS Mock AIME | Not measured | 86.7% | Only Qwen3.7 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 19.3% | Only Qwen3.7 Flash measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | 1081 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | 1092 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | 1097 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | 988 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | 1094 | Not measured | Only GPT-5.1-Codex-Mini measured |
| 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.7 Flash | Leader |
|---|---|---|---|
| IFBench | 67.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Long context reasoning (AA-LCR) | 66.7% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 44 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
Qwen3.7 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Flash 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 | Qwen3.7 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | July 27, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $0.25 | $0.03 |
| Output $/Mtok | $2.00 | $0.13 |
| 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 Qwen3.7 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-5.1-Codex-Mini or Qwen3.7 Flash?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Qwen3.7 Flash costs $0.03 and $0.13. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Qwen3.7 Flash?
Qwen3.7 Flash, with 1M tokens against 400K.
Has any benchmark tested both GPT-5.1-Codex-Mini and Qwen3.7 Flash?
No benchmark has tested both yet. GPT-5.1-Codex-Mini has results on LMArena WebDev, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Qwen3.7 Flash on ALE-Bench, Epoch Capabilities Index and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 21, 2026.