GPT-5.1-Codex-Max vs Summarize
Updated October 21, 2026. 0 benchmarks have a result for both. 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
- No data
- Output $/M
- No data
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 0
- GPT-5.1-Codex-Max only
- 6
- Summarize only
- 0
- Like-for-like categories
- 0 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
Long documents
Listed context window
Summarize
Summarize takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Summarize
GPT-5.1-Codex-Max $0.0063, Summarize $0.0000 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Summarize not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- GPT-5.1-Codex-Max $10.00 against Summarize Free per million output tokens.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Summarize 1M.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | 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 | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| Neither model has a result on 21 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | Leader |
|---|---|---|---|
| Neither model has a result on 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Summarize | 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
Summarize costs less
Repository review
50K input + 3K output tokens
Summarize costs less
Agent loop
200K input + 10K output tokens
Summarize 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 | ScaleDown Summarize |
|---|---|---|
| Maker | OpenAI | Scaledown |
| Weights | No public weights listed | No public weights listed |
| Released | December 4, 2025 | October 13, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.25 | $0.04 |
| Output $/Mtok | $10.00 | Free |
| 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 Summarize 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-Max or Summarize?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Summarize costs $0.04 and Free. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Summarize?
Summarize, with 1M tokens against 400K.
How many benchmarks is the GPT-5.1-Codex-Max vs Summarize comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Summarize's on 0. Where only one model has a result, the page says so instead of naming a winner.