GPT-5.1-Codex-Mini vs Step 3.5 Flash
GPT-5.1-Codex-Mini leads Step 3.5 Flash on the Artificial Analysis Intelligence Index, 20.4 to 17.0, and Step 3.5 Flash costs 6.7x less per output token ($0.30 vs $2.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Step 3.5 Flash: 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 τ²-Bench Telecom or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 7
- GPT-5.1-Codex-Mini only
- 15
- Step 3.5 Flash only
- 28
- Like-for-like categories
- 5 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 17.0.
Same board, both measured
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
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 3.5 Flash
GPT-5.1-Codex-Mini $0.0013, Step 3.5 Flash $0.0003 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Step 3.5 Flash not benchmarked.
- Widest category
- Agentic work, 31.6 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-5.1-Codex-Mini 62.9% against Step 3.5 Flash 94.4%, 31.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Step 3.5 Flash $0.30 per million output tokens, 6.7x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Step 3.5 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 3.5 Flash | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 2 | 0 | ||
| All shared boards | 4 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 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% | 32.6% | GPT-5.1-Codex-Mini |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena Text: coding | Not measured | 1436 | Only Step 3.5 Flash measured |
| LMArena Text: software and IT | Not measured | 1434 | Only Step 3.5 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 | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 17.0 | 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 index01
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 94.4% | Step 3.5 Flash |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 83.1% | Step 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 24.5% | Step 3.5 Flash |
| LMArena Text: hard prompts | Not measured | 1411 | Only Step 3.5 Flash measured |
| LMArena Text: expert | Not measured | 1421 | Only Step 3.5 Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1421 | Only Step 3.5 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, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 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 |
| LMArena Text: math | Not measured | 1408 | Only Step 3.5 Flash measured |
| LMArena Text: mathematical work | Not measured | 1421 | Only Step 3.5 Flash measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 Flash | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena Text: science | Not measured | 1415 | Only Step 3.5 Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1408 | Only Step 3.5 Flash measured |
| LMArena Text: legal and government | Not measured | 1408 | Only Step 3.5 Flash measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Step 3.5 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 Flash | Leader |
|---|---|---|---|
| IFBench | 67.9% | 66.5% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 66.7% | 63.0% | GPT-5.1-Codex-Mini |
| LMArena Text | Not measured | 1403 | Only Step 3.5 Flash measured |
| LMArena Text: instruction following | Not measured | 1385 | Only Step 3.5 Flash measured |
| LMArena Text: creative writing | Not measured | 1357 | Only Step 3.5 Flash measured |
| LMArena Text: English | Not measured | 1417 | Only Step 3.5 Flash measured |
| LMArena Text: non-English | Not measured | 1385 | Only Step 3.5 Flash measured |
| LMArena Text: Chinese | Not measured | 1446 | Only Step 3.5 Flash measured |
| LMArena Text: German | Not measured | 1405 | Only Step 3.5 Flash measured |
| LMArena Text: Japanese | Not measured | 1353 | Only Step 3.5 Flash measured |
| LMArena Text: Korean | Not measured | 1351 | Only Step 3.5 Flash measured |
| LMArena Text: French | Not measured | 1421 | Only Step 3.5 Flash measured |
| LMArena Text: Spanish | Not measured | 1419 | Only Step 3.5 Flash measured |
| LMArena Text: Russian | Not measured | 1385 | Only Step 3.5 Flash measured |
| LMArena Text: Polish | Not measured | 1389 | Only Step 3.5 Flash measured |
| LMArena Text: multi-turn | Not measured | 1404 | Only Step 3.5 Flash measured |
| LMArena Text: longer queries | Not measured | 1402 | Only Step 3.5 Flash measured |
| LMArena Text: writing and language | Not measured | 1372 | Only Step 3.5 Flash measured |
| LMArena Text: entertainment and media | Not measured | 1360 | Only Step 3.5 Flash measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Step 3.5 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 3.5 Flash costs less
Repository review
50K input + 3K output tokens
Step 3.5 Flash costs less
Agent loop
200K input + 10K output tokens
Step 3.5 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 3.5 Flash |
|---|---|---|
| Maker | OpenAI | StepFun |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | January 29, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.25 | $0.10 |
| Output $/Mtok | $2.00 | $0.30 |
| 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 3.5 Flash 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 τ²-Bench Telecom or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-5.1-Codex-Mini or Step 3.5 Flash?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Step 3.5 Flash?
GPT-5.1-Codex-Mini, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Step 3.5 Flash comparison based on?
7 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 3.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.