GPT-5.1-Codex-Mini vs Step 3.7 Flash
Updated October 21, 2026. 15 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.
GPT-5.1-Codex-Mini has the edge where the context window is larger, 400K against 262K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
- Shared results
- 15
- GPT-5.1-Codex-Mini only
- 7
- Step 3.7 Flash only
- 3
- Like-for-like categories
- 6 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
Intelligence
AA Intelligence Index
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 19.5.
Same board, both measured
Web design
Design Arena: websites
Step 3.7 Flash
Step 3.7 Flash leads, 1196 against 1111.
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
Step 3.7 Flash
GPT-5.1-Codex-Mini $0.0013, Step 3.7 Flash $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Step 3.7 Flash not benchmarked.
- Widest category
- Agentic work, 35.7 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-5.1-Codex-Mini 62.9% against Step 3.7 Flash 98.5%, 35.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Step 3.7 Flash $1.15 per million output tokens, 1.7x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Step 3.7 Flash 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 | Step 3.7 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| Web design | 0 | 8 | ||
| General | 1 | 1 | ||
| All shared boards | 3 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| AA Coding Index | Not measured | 39.6 | Only Step 3.7 Flash measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| SciCode | Not measured | 43.9% | Only Step 3.7 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 35.6% | Step 3.7 Flash |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| ALE-Bench | Not measured | 694.1 | Only Step 3.7 Flash measured |
| LMArena WebDev: HTML | 1253 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 19.5 | 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 index01
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 98.5% | Step 3.7 Flash |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 80.9% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 21.4% | Step 3.7 Flash |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 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 |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 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 designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | 1196 | Step 3.7 Flash |
| Design Arena: UI components | 1081 | 1179 | Step 3.7 Flash |
| Design Arena: game dev | 1092 | 1158 | Step 3.7 Flash |
| Design Arena: data viz | 1096 | 1172 | Step 3.7 Flash |
| Design Arena: 3D | 988 | 1129 | Step 3.7 Flash |
| Design Arena: all code categories | 1094 | 1182 | Step 3.7 Flash |
| Design Arena: SVG | 978 | 1085 | Step 3.7 Flash |
| Design Arena: ASCII art | 1104 | 1152 | Step 3.7 Flash |
| 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 | Step 3.7 Flash | Leader |
|---|---|---|---|
| IFBench | 67.9% | 67.3% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 66.7% | 73.7% | Step 3.7 Flash |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.7 Flash | 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
Step 3.7 Flash costs less
Repository review
50K input + 3K output tokens
Step 3.7 Flash costs less
Agent loop
200K input + 10K output tokens
Step 3.7 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 | StepFun Step 3.7 Flash |
|---|---|---|
| Maker | OpenAI | StepFun |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | May 28, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.25 | $0.20 |
| Output $/Mtok | $2.00 | $1.15 |
| 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 Step 3.7 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-5.1-Codex-Mini has the edge where the context window is larger, 400K against 262K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
Which is cheaper, GPT-5.1-Codex-Mini or Step 3.7 Flash?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Step 3.7 Flash costs $0.20 and $1.15. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Step 3.7 Flash?
GPT-5.1-Codex-Mini, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Step 3.7 Flash comparison based on?
15 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Step 3.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.