Math
Like-for-like- Gemini 3 Pro
- 32.9
- GPT-5.4 nano
- 21.0
- Weighted basis
- 2 vs 2 rows
- Reading
- Gemini 3 Pro leads
Model comparison
Gemini 3 Pro vs GPT-5.4 nano
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Gemini 3 Pro has the higher public score estimate, 66.85 versus 65.98, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 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 3 Pro
Gemini 3 Pro has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano 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
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-5.4 nano
GPT-5.4 nano 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Gemini 3 Pro only
4 Shared
9 GPT-5.4 nano only
- Shared results
- 4
- Gemini 3 Pro only
- 8
- GPT-5.4 nano only
- 9
- Like-for-like categories
- 1 / 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.
Multimodal
Directional only- Gemini 3 Pro
- 81.1
- GPT-5.4 nano
- 66.1
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 3 Pro
- Not measured
- GPT-5.4 nano
- 42.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3 Pro
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3 Pro
- 31.1
- GPT-5.4 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3 Pro
- Not measured
- GPT-5.4 nano
- 43.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3 Pro
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | GPT-5.4 nano | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 32.9 | 21.0 | Like-for-like2 vs 2 rows | Gemini 3 Pro leads |
| Multimodal | 81.1 | 66.1 | Directional only2 vs 1 rows | Directional only |
| Agentic | Not measured | 42.9 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | 31.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | Not measured | 43.8 | 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.
MMMU-Pro
Multimodal
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
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 Pro
- $0.008
- Fits in one request
- GPT-5.4 nano
- $0.00082
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3 Pro
- $0.136
- Fits in one request
- GPT-5.4 nano
- $0.01375
- Fits in one request
GPT-5.4 nano 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 Pro
- $0.56
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.4 nano
- $0.0205
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3 ProGPT-5.4 nano
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
GPT-5.4 nano
API model ID
Gemini 3 Pro
Not sourced
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3 Pro
Not published
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 3 Pro
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogDocumented outputs
Gemini 3 Pro
Not sourced
GPT-5.4 nano
Provider availability
Gemini 3 Pro
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemini 3 Pro
Non-Reasoning
GPT-5.4 nano
Reasoning
Weight access
Gemini 3 Pro
Proprietary
GPT-5.4 nano
Proprietary
License
Gemini 3 Pro
Proprietary
GPT-5.4 nano
Proprietary
Release date
Gemini 3 Pro
2025-11-18
GPT-5.4 nano
2026-03-17
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 3 Pro has the higher public score estimate, 66.85 versus 65.98, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.136 vs $0.01375. Cache-heavy agent loop: $0.56 vs $0.0205.
- Context tradeoff
- Gemini 3 Pro has the larger documented window (2M).
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 evidence21 rows
Agentic
Gert Labs
Gemini 3 Pro63.23%SourceGPT-5.4 nano—Not directly comparable
JobBench
Gemini 3 Pro11.4%SourceGPT-5.4 nano—Not directly comparable
Terminal-Bench 2.0
Gemini 3 Pro—GPT-5.4 nano46.3%SourceNot directly comparable
OSWorld-Verified
Gemini 3 Pro—GPT-5.4 nano39%SourceNot directly comparable
MCP Atlas
Gemini 3 Pro—GPT-5.4 nano56.1%SourceNot directly comparable
Toolathlon
Gemini 3 Pro—GPT-5.4 nano35.5%SourceNot directly comparable
τ²-bench results
Gemini 3 Pro—GPT-5.4 nano92.5%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGemini 3 Pro14.30%GPT-5.4 nano26.10%GPT-5.4 nano leads this result
Reasoning
ARC-AGI-2
Gemini 3 Pro31.1%SourceGPT-5.4 nano—Not directly comparable
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 3 Pro37.600%GPT-5.4 nano25.860%Gemini 3 Pro leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 3 Pro18.750%GPT-5.4 nano6.250%Gemini 3 Pro leads this result
Multimodal
MMMU-Pro
Gemini 3 Pro81%SourceGPT-5.4 nano66.1%SourceGemini 3 Pro leads this result
MathVision
Gemini 3 Pro86.6%SourceGPT-5.4 nano—Not directly comparable
VideoMMMU
Gemini 3 Pro87.6%SourceGPT-5.4 nano—Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%SourceGPT-5.4 nano—Not directly comparable
CharXiv
Gemini 3 Pro81.4%SourceGPT-5.4 nano—Not directly comparable
V*
Gemini 3 Pro88.0%SourceGPT-5.4 nano—Not directly comparable
MMMU-Pro w/ Python
Gemini 3 Pro—GPT-5.4 nano69.5%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3 Pro or GPT-5.4 nano?
Gemini 3 Pro has the higher public score estimate, 66.85 versus 65.98, 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 Pro or GPT-5.4 nano?
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 Pro or GPT-5.4 nano?
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 Pro or GPT-5.4 nano?
For the stated presets, chat costs $0.008 on Gemini 3 Pro and $0.00082 on GPT-5.4 nano; repository review costs $0.136 and $0.01375; the cache-heavy agent loop costs $0.56 and $0.0205. Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3 Pro or GPT-5.4 nano?
Gemini 3 Pro has the larger documented context window: 2M, compared with 400K.
Related comparisons
Last updated July 30, 2026
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