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
Gemini 2.5 Pro vs GPT-4.1 nano
Updated September 29, 2026. Rank says Gemini 2.5 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Gemini 2.5 Pro has the higher public score, 50.17 versus 24.89, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Gemini 2.5 Pro only
- 6
- GPT-4.1 nano only
- 2
- Like-for-like categories
- 1 / 8
Supported: Gemini 2.5 Pro · Estimated: GPT-4.1 nanoHow the comparison works
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.
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-ratesCoding 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: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 nano is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
24.4Gemini 2.5 Pro14.8GPT-4.1 nano
Directional only · BenchAlign v5.7
Gemini 2.5 Pro scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 Gemini 2.5 Pro only
2 Shared
2 GPT-4.1 nano only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
GPQAKnowledge
Normalized gap 32.7Gemini 2.5 Pro:83%GPT-4.1 nano:50.3%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 13.1Gemini 2.5 Pro:14.138%GPT-4.1 nano:1.034%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Knowledge
Like-for-like- Gemini 2.5 Pro
- 44.1
- Supported · #80/169
- GPT-4.1 nano
- 25.2
- Supported · #162/169
- Basis
- BenchAlign v5.7 lane · 2 vs 2 public rows
- Reading
- Gemini 2.5 Pro leads · intervals overlap
Coding
Directional only- Gemini 2.5 Pro
- 24.4
- Supported · #108/143
- GPT-4.1 nano
- 14.8
- Estimated · #137/143
- Basis
- BenchAlign v5.7 lane · 3 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemini 2.5 Pro
- 56.4
- #76/124
- GPT-4.1 nano
- 34.6
- #109/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 2.5 Pro
- 25.4
- Estimated · #91/117
- GPT-4.1 nano
- Not ranked
- Basis
- BenchAlign v5.7 lane · 1 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Pro
- 69.7
- Unranked · 2 rankable rows
- GPT-4.1 nano
- 36.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- 71.4
- Unranked · 1 rankable row
- GPT-4.1 nano
- 26.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not ranked
- GPT-4.1 nano
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 35.1
- Unranked · 2 rankable rows
- GPT-4.1 nano
- 25.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | GPT-4.1 nano | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 44.1Supported · #80/169 | 25.2Supported · #162/169 | Like-for-likeBenchAlign v5.7 lane · 2 vs 2 public rows | Gemini 2.5 Pro leads · intervals overlap |
| Coding | 24.4Supported · #108/143 | 14.8Estimated · #137/143 | Directional onlyBenchAlign v5.7 lane · 3 vs 0 public rows | Directional only |
| Instruction following | 56.4#76/124 | 34.6#109/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | 25.4Estimated · #91/117 | Not ranked | Not comparableBenchAlign v5.7 lane · 1 vs 0 public rows | Not comparable |
| Reasoning | 69.7Unranked · 2 rankable rows | 36.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 71.4Unranked · 1 rankable row | 26.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 35.1Unranked · 2 rankable rows | 25.4Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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 2.5 Pro
- $0.00625
- 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 2.5 Pro
- $0.0925
- 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 2.5 Pro
- $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.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 2.5 ProGPT-4.1 nano
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
GPT-4.1 nano
1M
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
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 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingGPT-4.1 nano
Not published
Documented inputs
Gemini 2.5 Pro
Not sourced
GPT-4.1 nano
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
GPT-4.1 nano
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
GPT-4.1 nano
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
GPT-4.1 nano
Non-Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
GPT-4.1 nano
Proprietary
License
Gemini 2.5 Pro
Proprietary
GPT-4.1 nano
Proprietary
Release date
Gemini 2.5 Pro
2025-03-01
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 2.5 Pro has the higher public score, 50.17 versus 24.89, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0925 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.
Questions
Which is better, Gemini 2.5 Pro or GPT-4.1 nano?
Gemini 2.5 Pro has the higher public score, 50.17 versus 24.89, 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 2.5 Pro or GPT-4.1 nano?
Gemini 2.5 Pro scores higher for coding on the public lane, 24.4 to 14.8. 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 2.5 Pro or GPT-4.1 nano?
GPT-4.1 nano is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 2.5 Pro or GPT-4.1 nano?
For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.0003 on GPT-4.1 nano; repository review costs $0.0925 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 2.5 Pro or GPT-4.1 nano?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence10 rows
Agentic
Gert Labs
Gemini 2.5 Pro42.01%SourceGPT-4.1 nano—Not directly comparable
Coding
Knowledge
Instruction following
IFEval
Gemini 2.5 Pro—GPT-4.1 nano83.2%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 2.5 Pro14.138%GPT-4.1 nano1.034%Gemini 2.5 Pro leads this result
FrontierMath v2 (Tier 4)
Gemini 2.5 Pro4.167%SourceGPT-4.1 nano—Not directly comparable
10 public results · 2 shared
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Last updated September 29, 2026