GPT-5.1-Codex-Max vs Step 3.5 Flash
Updated October 21, 2026. No benchmark has tested both models yet. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- 17.0
- Output $/M
- $0.30
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GPT-5.1-Codex-Max has the edge where the context window is larger, 400K against 262K tokens.
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Step 3.5 Flash on Terminal-Bench Hard (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. 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 Step 3.5 Flash has a AA Intelligence Index result, 17.0.
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
Step 3.5 Flash
GPT-5.1-Codex-Max $0.0063, Step 3.5 Flash $0.0003 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Step 3.5 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-Max $10.00 against Step 3.5 Flash $0.30 per million output tokens, 33.3x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Step 3.5 Flash 262K.
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 Index7 boards measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | 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 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Step 3.5 Flash measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | Not measured | Only GPT-5.1-Codex-Max 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 |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 17.0 | Only Step 3.5 Flash 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 measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 94.4% | Only Step 3.5 Flash 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 index5 boards measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 83.1% | Only Step 3.5 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 24.5% | Only Step 3.5 Flash measured |
| 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 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max 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 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | Leader |
|---|---|---|---|
| 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards measured
| Benchmark | GPT-5.1-Codex-Max | Step 3.5 Flash | Leader |
|---|---|---|---|
| IFBench | Not measured | 66.5% | Only Step 3.5 Flash measured |
| Long context reasoning (AA-LCR) | Not measured | 63.0% | Only Step 3.5 Flash measured |
| 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 | |||
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-Max | StepFun Step 3.5 Flash |
|---|---|---|
| Maker | OpenAI | StepFun |
| Weights | No public weights listed | Open weights |
| Released | December 4, 2025 | January 29, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.10 |
| Output $/Mtok | $10.00 | $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-Max better than Step 3.5 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-5.1-Codex-Max has the edge where the context window is larger, 400K against 262K tokens.
Which is cheaper, GPT-5.1-Codex-Max or Step 3.5 Flash?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 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-Max or Step 3.5 Flash?
GPT-5.1-Codex-Max, with 400K tokens against 262K.
Has any benchmark tested both GPT-5.1-Codex-Max and Step 3.5 Flash?
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Step 3.5 Flash on Terminal-Bench Hard (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead. Data as of October 21, 2026.