Knowledge
Like-for-like- Gemini 3.5 Flash-Lite
- 53.0
- Supported · #71/181
- GPT-4.1 nano
- 30.2
- Supported · #175/181
- Basis
- BenchAlign lane · 2 vs 2 public rows
- Reading
- Gemini 3.5 Flash-Lite leads · intervals overlap
Gemini 3.5 Flash-Lite vs GPT-4.1 nano
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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.
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
GPT-4.1 nano is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite and GPT-4.1 nano are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.
10 Gemini 3.5 Flash-Lite only
4 GPT-4.1 nano only
- Shared results
- 0
- Gemini 3.5 Flash-Lite only
- 10
- GPT-4.1 nano only
- 4
- Like-for-like categories
- 1 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Directional only- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- GPT-4.1 nano
- 32.0
- Estimated · #137/151
- Basis
- BenchAlign lane · 3 vs 0 public rows
- Reading
- Directional only
Coding
Directional only- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- GPT-4.1 nano
- 32.3
- Estimated · #164/183
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.8
- Unranked · 3 rankable rows
- GPT-4.1 nano
- 34.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GPT-4.1 nano
- 25.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GPT-4.1 nano
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.5 Flash-Lite
- 76.1
- #16/48
- GPT-4.1 nano
- 25.1
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- GPT-4.1 nano
- 36.0
- #106/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | GPT-4.1 nano | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 53.0Supported · #71/181 | 30.2Supported · #175/181 | Like-for-likeBenchAlign lane · 2 vs 2 public rows | Gemini 3.5 Flash-Lite leads · intervals overlap |
| Agentic | 43.4Estimated · #104/151 | 32.0Estimated · #137/151 | Directional onlyBenchAlign lane · 3 vs 0 public rows | Directional only |
| Coding | 43.6Supported · #121/183 | 32.3Estimated · #164/183 | Directional onlyBenchAlign lane · 4 vs 0 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 34.9Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 25.6Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.1#16/48 | 25.1Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 36.0#106/120 | 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.
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 3.5 Flash-Lite
- $0.00155
- 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-Lite
- $0.0225
- 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-Lite
- $0.037
- 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 Flash-LiteGPT-4.1 nano
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
GPT-4.1 nano
1M
API model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationGPT-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-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingGPT-4.1 nano
Not published
Documented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationGPT-4.1 nano
Not sourced
Documented outputs
Gemini 3.5 Flash-Lite
GPT-4.1 nano
Not sourced
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideGPT-4.1 nano
Not sourced
Reasoning profile
Gemini 3.5 Flash-Lite
Reasoning
GPT-4.1 nano
Non-Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
GPT-4.1 nano
Proprietary
License
Gemini 3.5 Flash-Lite
Proprietary
GPT-4.1 nano
Proprietary
Release date
Gemini 3.5 Flash-Lite
2026-07-21
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0225 vs $0.0062. Cache-heavy agent loop: $0.037 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 evidence14 rows
Agentic
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceGPT-4.1 nano—Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceGPT-4.1 nano—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceGPT-4.1 nano—Not directly comparable
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceGPT-4.1 nano—Not directly comparable
Reasoning
MRCRv2
Gemini 3.5 Flash-Lite72.2%SourceGPT-4.1 nano—Not directly comparable
Knowledge
GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%SourceGPT-4.1 nano—Not directly comparable
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%SourceGPT-4.1 nano—Not directly comparable
MMLU
Gemini 3.5 Flash-Lite—GPT-4.1 nano80.1%SourceNot directly comparable
GPQA
Gemini 3.5 Flash-Lite—GPT-4.1 nano50.3%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Gemini 3.5 Flash-Lite—GPT-4.1 nano1.034%SourceNot directly comparable
Instruction following
IFEval
Gemini 3.5 Flash-Lite—GPT-4.1 nano83.2%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.5 Flash-Lite or GPT-4.1 nano?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3.5 Flash-Lite or GPT-4.1 nano?
Gemini 3.5 Flash-Lite scores higher for coding on the public lane, 43.6 to 32.3. GPT-4.1 nano is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-4.1 nano?
Gemini 3.5 Flash-Lite scores higher for agentic tasks on the public lane, 43.4 to 32. Gemini 3.5 Flash-Lite and GPT-4.1 nano are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Gemini 3.5 Flash-Lite or GPT-4.1 nano?
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.0003 on GPT-4.1 nano; repository review costs $0.0225 and $0.0062; the cache-heavy agent loop costs $0.037 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-Lite or GPT-4.1 nano?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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