Agentic
Not comparable- Gemini 2.5 Pro
- Not measured
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Gemini 2.5 Pro vs GPT-5.1-Codex-Max
Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 2.5 Pro has the higher public score estimate, 57.16 versus 55.54, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Long documents
Prompts that approach the documented context limit
Gemini 2.5 Pro
Gemini 2.5 Pro has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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.
6 Gemini 2.5 Pro only
1 Shared
- Shared results
- 1
- Gemini 2.5 Pro only
- 6
- GPT-5.1-Codex-Max only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Gemini 2.5 Pro
- 63.8
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Pro
- Not measured
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 2.5 Pro
- 27.4
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 11.6
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not measured
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- Not measured
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Pro
- Not measured
- GPT-5.1-Codex-Max
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | GPT-5.1-Codex-Max | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | 63.8 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 27.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Math | 11.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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
- Gemini 2.5 Pro
- $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
- Gemini 2.5 Pro
- $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
- Gemini 2.5 Pro
- $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.
SpecificationGemini 2.5 ProGPT-5.1-Codex-Max
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
GPT-5.1-Codex-Max
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API pricingGPT-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.
Gemini 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingGPT-5.1-Codex-Max
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex-Max model documentationDocumented inputs
Gemini 2.5 Pro
Not sourced
GPT-5.1-Codex-Max
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
GPT-5.1-Codex-Max
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
GPT-5.1-Codex-Max
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
GPT-5.1-Codex-Max
Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
GPT-5.1-Codex-Max
Proprietary
License
Gemini 2.5 Pro
Proprietary
GPT-5.1-Codex-Max
Proprietary
Release date
Gemini 2.5 Pro
2025-03-01
GPT-5.1-Codex-Max
2025-11-19
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Gemini 2.5 Pro has the higher public score estimate, 57.16 versus 55.54, 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
- Gemini 2.5 Pro has the larger documented window (1M).
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 evidence7 rows
Agentic
Gert Labs
Gemini 2.5 Pro42.01%SourceGPT-5.1-Codex-Max—Not directly comparable
Coding
SWE-bench Verified
Gemini 2.5 Pro63.8%SourceGPT-5.1-Codex-Max—Not directly comparable
Vibe Code Bench
Shared sourceGemini 2.5 Pro0.40%GPT-5.1-Codex-Max22.17%GPT-5.1-Codex-Max leads this result
Knowledge
Math
Frequently asked questions
Which is better, Gemini 2.5 Pro or GPT-5.1-Codex-Max?
Gemini 2.5 Pro has the higher public score estimate, 57.16 versus 55.54, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 2.5 Pro or GPT-5.1-Codex-Max?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Gemini 2.5 Pro or GPT-5.1-Codex-Max?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Gemini 2.5 Pro or GPT-5.1-Codex-Max?
For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro 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, Gemini 2.5 Pro or GPT-5.1-Codex-Max?
Gemini 2.5 Pro has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 3, 2026
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