Math
Directional only- Gemini 3.5 Flash
- 32.9
- GPT-4.1 nano
- 1.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Gemini 3.5 Flash vs GPT-4.1 nano
Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.5 Flash has the higher public score, 64.67 versus 42.2, and the 90% score intervals do not overlap.
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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-4.1 nano
GPT-4.1 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-4.1 nano
GPT-4.1 nano has the lower estimated token cost for this stated workload. GPT-4.1 nano 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-4.1 nano
GPT-4.1 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
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.
22 Gemini 3.5 Flash only
1 Shared
3 GPT-4.1 nano only
- Shared results
- 1
- Gemini 3.5 Flash only
- 22
- GPT-4.1 nano only
- 3
- Like-for-like categories
- 0 / 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.
Agentic
Not comparable- Gemini 3.5 Flash
- 77.2
- GPT-4.1 nano
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 3.5 Flash
- 55.1
- GPT-4.1 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3.5 Flash
- 74.7
- GPT-4.1 nano
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3.5 Flash
- 40.2
- GPT-4.1 nano
- 50.3
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash
- Not measured
- GPT-4.1 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.5 Flash
- 83.8
- GPT-4.1 nano
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash
- Not measured
- GPT-4.1 nano
- 83.2
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash | GPT-4.1 nano | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 32.9 | 1.0 | Directional only2 vs 1 rows | Directional only |
| Agentic | 77.2 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 55.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 74.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Knowledge | 40.2 | 50.3 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 83.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | 83.2 | Not comparable0 vs 1 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 (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 3.5 Flash
- $0.006
- Fits in one request
- GPT-4.1 nano
- $0.0003
- Fits in one request
GPT-4.1 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.5 Flash
- $0.102
- Fits in one request
- GPT-4.1 nano
- $0.0062
- Fits in one request
GPT-4.1 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 3.5 Flash
- $0.15
- Fits in one request
- GPT-4.1 nano
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
GPT-4.1 nano has the lower modeled cost
GPT-4.1 nano 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 3.5 FlashGPT-4.1 nano
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash
GPT-4.1 nano
1M
API model ID
Gemini 3.5 Flash
gemini-3.5-flash
Google Gemini API pricingGPT-4.1 nano
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingGPT-4.1 nano
Not published
Documented inputs
Gemini 3.5 Flash
Not sourced
GPT-4.1 nano
Not sourced
Documented outputs
Gemini 3.5 Flash
Not sourced
GPT-4.1 nano
Not sourced
Provider availability
Gemini 3.5 Flash
Not sourced
GPT-4.1 nano
Not sourced
Reasoning profile
Gemini 3.5 Flash
Reasoning
GPT-4.1 nano
Non-Reasoning
Weight access
Gemini 3.5 Flash
Proprietary
GPT-4.1 nano
Proprietary
License
Gemini 3.5 Flash
Proprietary
GPT-4.1 nano
Proprietary
Release date
Gemini 3.5 Flash
2026-05-19
GPT-4.1 nano
2025-04-14
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 3.5 Flash has the higher public score, 64.67 versus 42.2, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.102 vs $0.0062. Cache-heavy agent loop: $0.15 vs $0.026.
- 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 evidence26 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash76.2%SourceGPT-4.1 nano—Not directly comparable
MCP Atlas
Gemini 3.5 Flash83.6%SourceGPT-4.1 nano—Not directly comparable
Toolathlon
Gemini 3.5 Flash56.5%SourceGPT-4.1 nano—Not directly comparable
OSWorld-Verified
Gemini 3.5 Flash78.4%SourceGPT-4.1 nano—Not directly comparable
Finance Agent v2
Gemini 3.5 Flash57.9%SourceGPT-4.1 nano—Not directly comparable
Gert Labs
Gemini 3.5 Flash61.85%SourceGPT-4.1 nano—Not directly comparable
ResearchClawBench
Gemini 3.5 Flash18.0%SourceGPT-4.1 nano—Not directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash76.2%SourceGPT-4.1 nano—Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash55.1%SourceGPT-4.1 nano—Not directly comparable
Vibe Code Bench
Gemini 3.5 Flash48.68%SourceGPT-4.1 nano—Not directly comparable
cursorBench31
Gemini 3.5 Flash49.8%SourceGPT-4.1 nano—Not directly comparable
cursorBench32
Gemini 3.5 Flash48.8%SourceGPT-4.1 nano—Not directly comparable
EEBench
Gemini 3.5 Flash34.3%SourceGPT-4.1 nano—Not directly comparable
Reasoning
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 3.5 Flash38.966%GPT-4.1 nano1.034%Gemini 3.5 Flash leads this result
FrontierMath v2 (Tier 4)
Gemini 3.5 Flash14.583%SourceGPT-4.1 nano—Not directly comparable
Multimodal
Instruction following
IFEval
Gemini 3.5 Flash—GPT-4.1 nano83.2%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.5 Flash or GPT-4.1 nano?
Gemini 3.5 Flash has the higher public score, 64.67 versus 42.2, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Gemini 3.5 Flash or GPT-4.1 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 3.5 Flash or GPT-4.1 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 3.5 Flash or GPT-4.1 nano?
For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.0003 on GPT-4.1 nano; repository review costs $0.102 and $0.0062; the cache-heavy agent loop costs $0.15 and $0.026. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3.5 Flash or GPT-4.1 nano?
Both models list the same context window, 1M.
Related comparisons
Last updated August 18, 2026
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