Agentic
Like-for-like- Gemini 3.6 Flash
- 50.7
- Supported · #60/151
- GPT-5.4 nano
- 37.3
- Supported · #127/151
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Gemini 3.6 Flash leads · intervals overlap
Gemini 3.6 Flash vs GPT-5.4 nano
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.6 Flash has the higher public score estimate, 70.11 versus 62.19, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
6 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.
Coding work
Code generation, repair, and software-engineering tasks
Gemini 3.6 Flash
Gemini 3.6 Flash leads on the public coding lane, 58.9 to 37.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Gemini 3.6 Flash
Gemini 3.6 Flash leads on the public agentic lane, 50.7 to 37.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 3.6 Flash
Gemini 3.6 Flash has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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. Costs use the listed standard API rates.
Confidence: listed-rates
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Gemini 3.6 Flash only
6 Shared
12 GPT-5.4 nano only
- Shared results
- 6
- Gemini 3.6 Flash only
- 2
- GPT-5.4 nano only
- 12
- Like-for-like categories
- 3 / 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
Like-for-like- Gemini 3.6 Flash
- 58.9
- Supported · #30/183
- GPT-5.4 nano
- 37.5
- Supported · #150/183
- Basis
- BenchAlign lane · 4 vs 3 public rows
- Reading
- Gemini 3.6 Flash leads · intervals overlap
Knowledge
Like-for-like- Gemini 3.6 Flash
- 68.6
- Supported · #18/181
- GPT-5.4 nano
- 48.8
- Supported · #97/181
- Basis
- BenchAlign lane · 2 vs 5 public rows
- Reading
- Gemini 3.6 Flash leads · intervals overlap
Reasoning
Not comparable- Gemini 3.6 Flash
- 77.8
- Unranked · 2 rankable rows
- GPT-5.4 nano
- 72.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.6 Flash
- Not ranked
- GPT-5.4 nano
- 43.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.6 Flash
- Not ranked
- GPT-5.4 nano
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.6 Flash
- 82.3
- Unranked · 1 rankable row
- GPT-5.4 nano
- 23.8
- #45/48
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.6 Flash
- Not ranked
- GPT-5.4 nano
- 92.9
- #9/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.6 Flash | GPT-5.4 nano | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.7Supported · #60/151 | 37.3Supported · #127/151 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | Gemini 3.6 Flash leads · intervals overlap |
| Coding | 58.9Supported · #30/183 | 37.5Supported · #150/183 | Like-for-likeBenchAlign lane · 4 vs 3 public rows | Gemini 3.6 Flash leads · intervals overlap |
| Knowledge | 68.6Supported · #18/181 | 48.8Supported · #97/181 | Like-for-likeBenchAlign lane · 2 vs 5 public rows | Gemini 3.6 Flash leads · intervals overlap |
| Reasoning | 77.8Unranked · 2 rankable rows | 72.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 43.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 82.3Unranked · 1 rankable row | 23.8#45/48 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.9#9/120 | 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.
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.
OSWorld-Verified
Agentic
Gemini 3.6 Flash: 83%GPT-5.4 nano: 39%Normalized gap 44.0Gemini 3.6 Flash source GPT-5.4 nano sourceMMLU-Pro (Vals)
Knowledge
Gemini 3.6 Flash: 89.3%GPT-5.4 nano: 77.2%Normalized gap 12.1Gemini 3.6 Flash source GPT-5.4 nano sourceLiveCodeBench (Vals)
Coding
Gemini 3.6 Flash: 88.1%GPT-5.4 nano: 84.0%Normalized gap 4.1Gemini 3.6 Flash source GPT-5.4 nano source
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 3.6 Flash
- $0.00525
- 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 3.6 Flash
- $0.0975
- 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 3.6 Flash
- $0.135
- Fits in one request
- GPT-5.4 nano
- $0.0205
- Fits in one request
GPT-5.4 nano 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 3.6 FlashGPT-5.4 nano
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.6 Flash
GPT-5.4 nano
API model ID
Gemini 3.6 Flash
gemini-3.6-flash
Google Gemini 3.6 Flash model documentationGPT-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 3.6 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingGPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 3.6 Flash
text, image, video, audio, pdf
Google Gemini 3.6 Flash model documentationGPT-5.4 nano
text, image
OpenAI model catalogDocumented outputs
Gemini 3.6 Flash
GPT-5.4 nano
Provider availability
Gemini 3.6 Flash
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideGPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Gemini 3.6 Flash
Reasoning
GPT-5.4 nano
Reasoning
Weight access
Gemini 3.6 Flash
Proprietary
GPT-5.4 nano
Proprietary
License
Gemini 3.6 Flash
Proprietary
GPT-5.4 nano
Proprietary
Release date
Gemini 3.6 Flash
2026-07-21
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 3.6 Flash has the higher public score estimate, 70.11 versus 62.19, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0975 vs $0.01375. Cache-heavy agent loop: $0.135 vs $0.0205.
- Context tradeoff
- Gemini 3.6 Flash 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 evidence20 rows
Agentic
OSWorld-Verified
Gemini 3.6 Flash83%SourceGPT-5.4 nano39%SourceGemini 3.6 Flash leads this result
Terminal-Bench 2.1 (Vals)
Gemini 3.6 Flash73.8%SourceGPT-5.4 nano41.6%SourceGemini 3.6 Flash leads this result
Terminal-Bench 2.0
Gemini 3.6 Flash—GPT-5.4 nano46.3%SourceNot directly comparable
MCP Atlas
Gemini 3.6 Flash—GPT-5.4 nano56.1%SourceNot directly comparable
Toolathlon
Gemini 3.6 Flash—GPT-5.4 nano35.5%SourceNot directly comparable
τ²-bench results
Gemini 3.6 Flash—GPT-5.4 nano92.5%SourceNot directly comparable
Coding
deepSwe
Gemini 3.6 Flash49%SourceGPT-5.4 nano—Not directly comparable
cursorBench32
Gemini 3.6 Flash53.5%SourceGPT-5.4 nano—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.6 Flash88.1%SourceGPT-5.4 nano84.0%SourceGemini 3.6 Flash leads this result
SWE-bench (Vals)
Gemini 3.6 Flash79.6%SourceGPT-5.4 nano69.8%SourceGemini 3.6 Flash leads this result
Vibe Code Bench
Gemini 3.6 Flash—GPT-5.4 nano26.10%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Gemini 3.6 Flash93.4%SourceGPT-5.4 nano77.5%SourceGemini 3.6 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.6 Flash89.3%SourceGPT-5.4 nano77.2%SourceGemini 3.6 Flash leads this result
GPQA
Gemini 3.6 Flash—GPT-5.4 nano82.8%SourceNot directly comparable
HLE
Gemini 3.6 Flash—GPT-5.4 nano37.7%SourceNot directly comparable
HLE w/o tools
Gemini 3.6 Flash—GPT-5.4 nano24.3%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Gemini 3.6 Flash or GPT-5.4 nano?
Gemini 3.6 Flash has the higher public score estimate, 70.11 versus 62.19, 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 3.6 Flash or GPT-5.4 nano?
Gemini 3.6 Flash leads the public coding lane, 58.9 to 37.5, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Gemini 3.6 Flash or GPT-5.4 nano?
Gemini 3.6 Flash leads the public agentic tasks lane, 50.7 to 37.3, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Gemini 3.6 Flash or GPT-5.4 nano?
For the stated presets, chat costs $0.00525 on Gemini 3.6 Flash and $0.00082 on GPT-5.4 nano; repository review costs $0.0975 and $0.01375; the cache-heavy agent loop costs $0.135 and $0.0205. Costs use the listed standard API rates.
Which has the larger context window, Gemini 3.6 Flash or GPT-5.4 nano?
Gemini 3.6 Flash has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 4, 2026
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