Knowledge
Like-for-like- GPT-4.1
- 40.7
- Supported · #135/181
- GPT-4.1 nano
- 30.2
- Supported · #175/181
- Basis
- BenchAlign lane · 2 vs 2 public rows
- Reading
- GPT-4.1 leads · intervals overlap
GPT-4.1 vs GPT-4.1 nano
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
Decision reading
GPT-4.1 has the higher public score estimate, 43.81 versus 28.43, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 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 has no published cached-input rate, so cached tokens use its listed input rate. 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
GPT-4.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
3 GPT-4.1 only
4 Shared
- Shared results
- 4
- GPT-4.1 only
- 3
- GPT-4.1 nano only
- 0
- 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.
Coding
Directional only- GPT-4.1
- 38.8
- Supported · #143/183
- GPT-4.1 nano
- 32.3
- Estimated · #164/183
- Basis
- BenchAlign lane · 1 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-4.1
- 50.3
- #80/120
- GPT-4.1 nano
- 36.0
- #106/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4.1
- Not ranked
- GPT-4.1 nano
- 32.0
- Estimated · #137/151
- Basis
- BenchAlign lane · 1 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1
- 67.2
- Unranked · 2 rankable rows
- GPT-4.1 nano
- 34.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1
- 28.1
- Unranked · 2 rankable rows
- GPT-4.1 nano
- 25.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1
- Not ranked
- GPT-4.1 nano
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1
- 50.2
- Unranked · 1 rankable row
- GPT-4.1 nano
- 25.3
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 | GPT-4.1 nano | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 40.7Supported · #135/181 | 30.2Supported · #175/181 | Like-for-likeBenchAlign lane · 2 vs 2 public rows | GPT-4.1 leads · intervals overlap |
| Coding | 38.8Supported · #143/183 | 32.3Estimated · #164/183 | Directional onlyBenchAlign lane · 1 vs 0 public rows | Directional only |
| Instruction following | 50.3#80/120 | 36.0#106/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 32.0Estimated · #137/151 | Not comparableBenchAlign lane · 1 vs 0 public rows | Not comparable |
| Reasoning | 67.2Unranked · 2 rankable rows | 34.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 28.1Unranked · 2 rankable rows | 25.6Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 50.2Unranked · 1 rankable row | 25.3Unranked · 1 rankable row | 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.
GPQA
Knowledge
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
- GPT-4.1
- $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
- GPT-4.1
- $0.124
- 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
- GPT-4.1
- $0.52
- Fits in one request
- Cached input priced at the published list-input rate
- 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 has no published cached-input rate, so cached tokens use its listed input rate. 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.
SpecificationGPT-4.1GPT-4.1 nano
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1
1M
GPT-4.1 nano
1M
API model ID
GPT-4.1
Not sourced
GPT-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.
GPT-4.1
Not published
GPT-4.1 nano
Not published
Documented inputs
GPT-4.1
Not sourced
GPT-4.1 nano
Not sourced
Documented outputs
GPT-4.1
Not sourced
GPT-4.1 nano
Not sourced
Provider availability
GPT-4.1
Not sourced
GPT-4.1 nano
Not sourced
Reasoning profile
GPT-4.1
Non-Reasoning
GPT-4.1 nano
Non-Reasoning
Weight access
GPT-4.1
Proprietary
GPT-4.1 nano
Proprietary
License
GPT-4.1
Proprietary
GPT-4.1 nano
Proprietary
Release date
GPT-4.1
2025-04-14
GPT-4.1 nano
2025-04-14
If you are choosing between sibling variants
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-4.1 has the higher public score estimate, 43.81 versus 28.43, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.124 vs $0.0062. Cache-heavy agent loop: $0.52 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 evidence7 rows
Agentic
Gert Labs
GPT-4.125.65%SourceGPT-4.1 nano—Not directly comparable
Coding
SWE-bench Verified
GPT-4.154.6%SourceGPT-4.1 nano—Not directly comparable
Knowledge
MMLU
Shared sourceGPT-4.190.2%GPT-4.1 nano80.1%GPT-4.1 leads this result
GPQA
Shared sourceGPT-4.166.3%GPT-4.1 nano50.3%GPT-4.1 leads this result
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-4.15.517%GPT-4.1 nano1.034%GPT-4.1 leads this result
FrontierMath v2 (Tier 4)
GPT-4.10.000%SourceGPT-4.1 nano—Not directly comparable
Instruction following
IFEval
Shared sourceGPT-4.187.4%GPT-4.1 nano83.2%GPT-4.1 leads this result
Frequently asked questions
Which is better, GPT-4.1 or GPT-4.1 nano?
GPT-4.1 has the higher public score estimate, 43.81 versus 28.43, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-4.1 or GPT-4.1 nano?
GPT-4.1 scores higher for coding on the public lane, 38.8 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, GPT-4.1 or GPT-4.1 nano?
GPT-4.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 or GPT-4.1 nano?
For the stated presets, chat costs $0.006 on GPT-4.1 and $0.0003 on GPT-4.1 nano; repository review costs $0.124 and $0.0062; the cache-heavy agent loop costs $0.52 and $0.026. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 or GPT-4.1 nano?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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