Updated September 29, 2026. Rank says GPT-4.1 nano 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
GPT-4.1 nano has the higher public score estimate, 24.89 versus 22.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 nano and Llama 4 Maverick are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
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.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Directional only · BenchAlign v5.7
Llama 4 Maverick 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Normalized gap 0.3Each 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.
| Category | GPT-4.1 nano | Llama 4 Maverick | Basis | Reading |
|---|---|---|---|---|
| Coding | 14.8Estimated · #137/143 | 18.1Estimated · #135/143 | Directional onlyBenchAlign v5.7 lane · 0 vs 0 public rows | Directional only |
| Knowledge | 25.2Supported · #162/169 | 31.0Estimated · #137/169 | Directional onlyBenchAlign v5.7 lane · 2 vs 0 public rows | Directional only |
| Instruction following | 34.6#109/124 | 48.9#82/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 12.8Estimated · #112/117 | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 36.1Unranked · 2 rankable rows | 56.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 26.4Unranked · 1 rankable row | 52.6Unranked · 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 | 25.4Unranked · 1 rankable row | 25.1Unranked · 1 rankable row | Not comparableProvisional lane · 1 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.
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.
1K fresh input + 500 output tokens
Llama 4 Maverick has no comparable published API token rate.
50K fresh input + 3K output tokens
Llama 4 Maverick has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. Llama 4 Maverick has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
Llama 4 Maverick
1M
GPT-4.1 nano
Not sourced
Llama 4 Maverick
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-4.1 nano
Not published
Llama 4 Maverick
No comparable hosted API rate
GPT-4.1 nano
Not sourced
Llama 4 Maverick
Not sourced
GPT-4.1 nano
Not sourced
Llama 4 Maverick
Not sourced
GPT-4.1 nano
Not sourced
Llama 4 Maverick
Not sourced
GPT-4.1 nano
Non-Reasoning
Llama 4 Maverick
Non-Reasoning
GPT-4.1 nano
Proprietary
Llama 4 Maverick
Open Weight
GPT-4.1 nano
Proprietary
Llama 4 Maverick
Open Weight
GPT-4.1 nano
2025-04-14
Llama 4 Maverick
2026-02-28
GPT-4.1 nano has the higher public score estimate, 24.89 versus 22.94, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Llama 4 Maverick scores higher for coding on the public lane, 18.1 to 14.8. GPT-4.1 nano and Llama 4 Maverick are 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.
GPT-4.1 nano is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
IFEval
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-4.1 nano leads this result
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Last updated September 29, 2026
