Reasoning
Like-for-like- Gemini 3.1 Pro
- 77.1
- GPT-5.5
- 85.0
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.5 leads
Model comparison
Gemini 3.1 Pro vs GPT-5.5
Updated July 28, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.5 has the higher public score estimate, 72.04 versus 54.66, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
12 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.
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.1 Pro
Gemini 3.1 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.1 Pro
Gemini 3.1 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.1 Pro
Gemini 3.1 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
11 Gemini 3.1 Pro only
12 Shared
21 GPT-5.5 only
- Shared results
- 12
- Gemini 3.1 Pro only
- 11
- GPT-5.5 only
- 21
- Like-for-like categories
- 2 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Math
Like-for-like- Gemini 3.1 Pro
- 31.8
- GPT-5.5
- 47.6
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.5 leads
Multimodal
Directional only- Gemini 3.1 Pro
- 82.6
- GPT-5.5
- 70.4
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 3.1 Pro
- Not measured
- GPT-5.5
- 81.6
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3.1 Pro
- Not measured
- GPT-5.5
- 58.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3.1 Pro
- Not measured
- GPT-5.5
- 57.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.1 Pro
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.1 Pro
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 3.1 Pro | GPT-5.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Reasoning | 77.1 | 85.0 | Like-for-like1 vs 1 rows | GPT-5.5 leads |
| Math | 31.8 | 47.6 | Like-for-like2 vs 2 rows | GPT-5.5 leads |
| Multimodal | 82.6 | 70.4 | Directional only2 vs 2 rows | Directional only |
| Agentic | Not measured | 81.6 | Not comparable0 vs 3 rows | Not comparable |
| Coding | Not measured | 58.6 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 57.8 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
ARC-AGI-2
Reasoning
MMMU-Pro
Multimodal
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 3.1 Pro
- $0.008
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Gemini 3.1 Pro has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.1 Pro
- $0.136
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Gemini 3.1 Pro has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.1 Pro
- $0.2
- Fits in one request
- GPT-5.5
- $0.5
- Fits in one request
Gemini 3.1 Pro has the lower modeled cost
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 3.1 ProGPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.1 Pro
GPT-5.5
API model ID
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationGPT-5.5
gpt-5.5
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingGPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationGPT-5.5
Not sourced
Documented outputs
Gemini 3.1 Pro
GPT-5.5
Not sourced
Provider availability
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogGPT-5.5
Not sourced
Reasoning profile
Gemini 3.1 Pro
Reasoning
GPT-5.5
Reasoning
Weight access
Gemini 3.1 Pro
Proprietary
GPT-5.5
Proprietary
License
Gemini 3.1 Pro
Proprietary
GPT-5.5
Proprietary
Release date
Gemini 3.1 Pro
2026-02-19
GPT-5.5
2026-04-23
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
- GPT-5.5 has the higher public score estimate, 72.04 versus 54.66, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.136 vs $0.34. Cache-heavy agent loop: $0.2 vs $0.5.
- Context tradeoff
- Both models list 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 evidence44 rows
Agentic
Claw-Eval
Gemini 3.1 Pro57.8%SourceGPT-5.5—Not directly comparable
DeepSearchQA
Gemini 3.1 Pro69.7%SourceGPT-5.5—Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%SourceGPT-5.598%SourceGPT-5.5 leads this result
Gert Labs
Shared sourceGemini 3.1 Pro56.87%GPT-5.572.93%GPT-5.5 leads this result
ResearchClawBench
Shared sourceGemini 3.1 Pro13.3%GPT-5.517.0%GPT-5.5 leads this result
Terminal-Bench 2.0
Gemini 3.1 Pro—GPT-5.582%SourceNot directly comparable
CyberGym
Gemini 3.1 Pro—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Gemini 3.1 Pro—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Gemini 3.1 Pro—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Gemini 3.1 Pro—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Gemini 3.1 Pro—GPT-5.555.6%SourceNot directly comparable
OSWorld 2.0
Gemini 3.1 Pro—GPT-5.513.0%SourceNot directly comparable
JobBench
Gemini 3.1 Pro—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Gemini 3.1 Pro—GPT-5.513.4%SourceNot directly comparable
Coding
LiveCodeBench Pro
Gemini 3.1 Pro82.9%SourceGPT-5.5—Not directly comparable
React Native Evals
Shared sourceGemini 3.1 Pro78.9%GPT-5.584.7%GPT-5.5 leads this result
Vibe Code Bench
Shared sourceGemini 3.1 Pro32.03%GPT-5.569.85%GPT-5.5 leads this result
SWE-bench Pro
Gemini 3.1 Pro—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—GPT-5.582.0%SourceNot directly comparable
cursorBench31
Gemini 3.1 Pro—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Gemini 3.1 Pro—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
Gemini 3.1 Pro—GPT-5.543.0%SourceNot directly comparable
Reasoning
ARC-AGI-2
Gemini 3.1 Pro77.1%SourceGPT-5.585%SourceGPT-5.5 leads this result
ARC-AGI-3
Shared sourceGemini 3.1 Pro0.4%GPT-5.50.4%GPT-5.5 leads this result
MRCR v2 64K-128K
Gemini 3.1 Pro—GPT-5.583.1%SourceNot directly comparable
MRCR v2 128K-256K
Gemini 3.1 Pro—GPT-5.587.5%SourceNot directly comparable
Knowledge
GPQA-D
Gemini 3.1 Pro94.3%SourceGPT-5.593.6%SourceGemini 3.1 Pro leads this result
HLE w/o tools
Gemini 3.1 Pro45.4%SourceGPT-5.541.4%SourceGemini 3.1 Pro leads this result
HealthBench Hard
Gemini 3.1 Pro20.6%SourceGPT-5.5—Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%SourceGPT-5.5—Not directly comparable
GPQA
Gemini 3.1 Pro—GPT-5.593.6%SourceNot directly comparable
HLE
Gemini 3.1 Pro—GPT-5.552.2%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 3.1 Pro36.900%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 3.1 Pro16.700%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
Gemini 3.1 Pro—GPT-5.551.7%SourceNot directly comparable
Multimodal
MMMU-Pro
Gemini 3.1 Pro83.9%SourceGPT-5.581.2%SourceGemini 3.1 Pro leads this result
CharXiv
Gemini 3.1 Pro80.2%SourceGPT-5.5—Not directly comparable
ERQA
Gemini 3.1 Pro69.4%SourceGPT-5.5—Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%SourceGPT-5.5—Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%SourceGPT-5.5—Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%SourceGPT-5.5—Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%SourceGPT-5.5—Not directly comparable
MMMU-Pro w/ Python
Gemini 3.1 Pro—GPT-5.583.2%SourceNot directly comparable
OfficeQA Pro
Gemini 3.1 Pro—GPT-5.554.1%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.1 Pro or GPT-5.5?
GPT-5.5 has the higher public score estimate, 72.04 versus 54.66, 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 3.1 Pro or GPT-5.5?
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 3.1 Pro or GPT-5.5?
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 3.1 Pro or GPT-5.5?
For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.02 on GPT-5.5; repository review costs $0.136 and $0.34; the cache-heavy agent loop costs $0.2 and $0.5. Costs use the listed standard API rates.
Which has the larger context window, Gemini 3.1 Pro or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated July 28, 2026
Keep the decision current
One weekly note on benchmark changes, pricing moves, and models worth re-testing.