Long documents
Prompts that approach the documented context limit
GPT-4.1 nano
GPT-4.1 nano has the larger documented context window.
Confidence: documented
GPT-4.1 nano vs MiniMax M1 80k
Updated September 23, 2026. Rank says MiniMax M1 80k 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
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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.
- Shared results
- 0
- GPT-4.1 nano only
- 4
- MiniMax M1 80k only
- 0
- Like-for-like categories
- 0 / 8
Estimated: GPT-4.1 nano and MiniMax M1 80kHow 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiniMax M1 80k is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 nano and MiniMax M1 80k are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. MiniMax M1 80k does not fit this workload in one request. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M1 80k has no comparable published API token rate.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
20.5GPT-4.1 nano—MiniMax M1 80k
Not comparable · BenchAlign v5.6
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
4 GPT-4.1 nano only
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.6 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
Not comparable- GPT-4.1 nano
- Not ranked
- MiniMax M1 80k
- Not ranked
- Basis
- BenchAlign v5.6 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-4.1 nano
- 20.5
- Estimated · #125/135
- MiniMax M1 80k
- Not ranked
- Basis
- BenchAlign v5.6 lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1 nano
- 35.0
- Unranked · 2 rankable rows
- MiniMax M1 80k
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 nano
- 25.4
- Unranked · 1 rankable row
- MiniMax M1 80k
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4.1 nano
- 23.8
- Supported · #152/160
- MiniMax M1 80k
- Not ranked
- Basis
- BenchAlign v5.6 lane · 2 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 nano
- Not ranked
- MiniMax M1 80k
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1 nano
- 34.6
- #109/124
- MiniMax M1 80k
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 nano
- 25.4
- Unranked · 1 rankable row
- MiniMax M1 80k
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 nano | MiniMax M1 80k | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.6 lane · 0 vs 0 public rows | Not comparable |
| Coding | 20.5Estimated · #125/135 | Not ranked | Not comparableBenchAlign v5.6 lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 35.0Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 25.4Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 23.8Supported · #152/160 | Not ranked | Not comparableBenchAlign v5.6 lane · 2 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 34.6#109/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 25.4Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) 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
- GPT-4.1 nano
- $0.0003
- Fits in one request
- MiniMax M1 80k
- API rate not published
- Fits in one request
MiniMax M1 80k has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 nano
- $0.0062
- Fits in one request
- MiniMax M1 80k
- API rate not published
- Fits in one request
MiniMax M1 80k has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-4.1 nano
- $0.026
- Fits in one request
- Cached input priced at the published list-input rate
- MiniMax M1 80k
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
MiniMax M1 80k does not fit this workload in one request. GPT-4.1 nano has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M1 80k has no comparable published API token 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.
SpecificationGPT-4.1 nanoMiniMax M1 80k
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 nano
1M
MiniMax M1 80k
80K
API model ID
GPT-4.1 nano
Not sourced
MiniMax M1 80k
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 nano
Not published
MiniMax M1 80k
No comparable hosted API rate
Documented inputs
GPT-4.1 nano
Not sourced
MiniMax M1 80k
Not sourced
Documented outputs
GPT-4.1 nano
Not sourced
MiniMax M1 80k
Not sourced
Provider availability
GPT-4.1 nano
Not sourced
MiniMax M1 80k
Not sourced
Reasoning profile
GPT-4.1 nano
Non-Reasoning
MiniMax M1 80k
Non-Reasoning
Weight access
GPT-4.1 nano
Proprietary
MiniMax M1 80k
Proprietary
License
GPT-4.1 nano
Proprietary
MiniMax M1 80k
Proprietary
Release date
GPT-4.1 nano
2025-04-14
MiniMax M1 80k
2025-01-15
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-4.1 nano has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-4.1 nano or MiniMax M1 80k?
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, GPT-4.1 nano or MiniMax M1 80k?
MiniMax M1 80k is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-4.1 nano or MiniMax M1 80k?
GPT-4.1 nano and MiniMax M1 80k are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 nano or MiniMax M1 80k?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-4.1 nano or MiniMax M1 80k?
GPT-4.1 nano has the larger documented context window: 1M, compared with 80K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence4 rows
4 public results · 0 shared
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Last updated September 23, 2026