Agentic
Not comparable- GPT-5.1-Codex
- 51.4
- Estimated · #54/151
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- BenchAlign lane · 2 vs 0 public rows
- Reading
- Not comparable
GPT-5.1-Codex vs GPT-5.1-Codex-Max
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.1-Codex-Max has the higher public score estimate, 53.95 versus 51.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.1-Codex-Max is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.1-Codex-Max is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 GPT-5.1-Codex only
1 Shared
- Shared results
- 1
- GPT-5.1-Codex only
- 2
- GPT-5.1-Codex-Max only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- GPT-5.1-Codex
- 47.4
- Estimated · #89/183
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- BenchAlign lane · 1 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1-Codex
- 70.6
- Unranked · 2 rankable rows
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.1-Codex
- 53.3
- Estimated · #69/181
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1-Codex
- 66.8
- Unranked · 1 rankable row
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.1-Codex
- 85.3
- #42/120
- GPT-5.1-Codex-Max
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.1-Codex | GPT-5.1-Codex-Max | Basis | Reading |
|---|---|---|---|---|
| Agentic | 51.4Estimated · #54/151 | Not ranked | Not comparableBenchAlign lane · 2 vs 0 public rows | Not comparable |
| Coding | 47.4Estimated · #89/183 | Not ranked | Not comparableBenchAlign lane · 1 vs 1 public rows | Not comparable |
| Reasoning | 70.6Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 53.3Estimated · #69/181 | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 66.8Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 85.3#42/120 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GPT-5.1-Codex
- $0.00625
- Fits in one request
- GPT-5.1-Codex-Max
- $0.00625
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.1-Codex
- $0.0925
- Fits in one request
- GPT-5.1-Codex-Max
- $0.0925
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.1-Codex
- $0.15
- Fits in one request
- GPT-5.1-Codex-Max
- $0.15
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.1-CodexGPT-5.1-Codex-Max
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1-Codex
GPT-5.1-Codex-Max
API model ID
GPT-5.1-Codex
gpt-5.1-codex
OpenAI GPT-5.1-Codex model documentationGPT-5.1-Codex-Max
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.1-Codex
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex model documentationGPT-5.1-Codex-Max
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex-Max model documentationDocumented inputs
GPT-5.1-Codex
Not sourced
GPT-5.1-Codex-Max
Not sourced
Documented outputs
GPT-5.1-Codex
Not sourced
GPT-5.1-Codex-Max
Not sourced
Provider availability
GPT-5.1-Codex
Not sourced
GPT-5.1-Codex-Max
Not sourced
Reasoning profile
GPT-5.1-Codex
Reasoning
GPT-5.1-Codex-Max
Reasoning
Weight access
GPT-5.1-Codex
Proprietary
GPT-5.1-Codex-Max
Proprietary
License
GPT-5.1-Codex
Proprietary
GPT-5.1-Codex-Max
Proprietary
Release date
GPT-5.1-Codex
2025-10-15
GPT-5.1-Codex-Max
2025-11-19
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.1-Codex-Max has the higher public score estimate, 53.95 versus 51.62, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0925 vs $0.0925. Cache-heavy agent loop: $0.15 vs $0.15.
- Context tradeoff
- Both models list 400K.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence3 rows
Agentic
Coding
Vibe Code Bench
Shared sourceGPT-5.1-Codex13.12%GPT-5.1-Codex-Max22.17%GPT-5.1-Codex-Max leads this result
Frequently asked questions
Which is better, GPT-5.1-Codex or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max has the higher public score estimate, 53.95 versus 51.62, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.1-Codex or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.1-Codex or GPT-5.1-Codex-Max?
GPT-5.1-Codex-Max is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.1-Codex or GPT-5.1-Codex-Max?
For the stated presets, chat costs $0.00625 on GPT-5.1-Codex and $0.00625 on GPT-5.1-Codex-Max; repository review costs $0.0925 and $0.0925; the cache-heavy agent loop costs $0.15 and $0.15. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.1-Codex or GPT-5.1-Codex-Max?
Both models list the same context window, 400K.
Related comparisons
Last updated September 4, 2026
Watch GPT-5.1-Codex vs GPT-5.1-Codex-Max
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.