Long documents
Prompts that approach the documented context limit
GPT-5.4 nano
GPT-5.4 nano has the larger documented context window.
Confidence: documented
GPT-5.4 nano vs MiniMax M2.5
Updated October 29, 2026. Rank says MiniMax M2.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
MiniMax M2.5 has the higher public score estimate, 49.17 versus 46.15, 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.
- Shared results
- 1
- GPT-5.4 nano only
- 22
- MiniMax M2.5 only
- 0
- Like-for-like categories
- 0 / 8
Supported: GPT-5.4 nano and MiniMax M2.5How 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-5.4 nano
GPT-5.4 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
MiniMax M2.5 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
MiniMax M2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-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 M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
30.1GPT-5.4 nano34.5MiniMax M2.5
Directional only · BenchAlign v5.8
MiniMax M2.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
22 GPT-5.4 nano only
1 Shared
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.
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 v5.8 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- GPT-5.4 nano
- 33.7
- Supported · #97/135
- MiniMax M2.5
- 30.1
- Estimated · #109/135
- Basis
- BenchAlign v5.8 lane · 6 vs 0 public rows
- Reading
- Directional only
Coding
Directional only- GPT-5.4 nano
- 30.1
- Supported · #100/156
- MiniMax M2.5
- 34.5
- Estimated · #87/156
- Basis
- BenchAlign v5.8 lane · 4 vs 1 public rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.4 nano
- 40.6
- Unranked · 4 rankable rows
- MiniMax M2.5
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 52.6
- #51/58
- MiniMax M2.5
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.4 nano
- 39.4
- Supported · #117/179
- MiniMax M2.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 5 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not ranked
- MiniMax M2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 nano
- 93.3
- #12/157
- MiniMax M2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 52.3
- Unranked · 2 rankable rows
- MiniMax M2.5
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | MiniMax M2.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 33.7Supported · #97/135 | 30.1Estimated · #109/135 | Directional onlyBenchAlign v5.8 lane · 6 vs 0 public rows | Directional only |
| Coding | 30.1Supported · #100/156 | 34.5Estimated · #87/156 | Directional onlyBenchAlign v5.8 lane · 4 vs 1 public rows | Directional only |
| Reasoning | 40.6Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 52.6#51/58 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 39.4Supported · #117/179 | Not ranked | Not comparableBenchAlign v5.8 lane · 5 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 93.3#12/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 52.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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-5.4 nano
- $0.00082
- Fits in one request
- MiniMax M2.5
- $0.0009
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.4 nano
- $0.01375
- Fits in one request
- MiniMax M2.5
- $0.0186
- Fits in one request
GPT-5.4 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-5.4 nano
- $0.0205
- Fits in one request
- MiniMax M2.5
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 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.
SpecificationGPT-5.4 nanoMiniMax M2.5
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
MiniMax M2.5
128K
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationMiniMax M2.5
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingMiniMax M2.5
Not published
Documented inputs
GPT-5.4 nano
text, image
OpenAI model catalogMiniMax M2.5
Not sourced
Documented outputs
GPT-5.4 nano
MiniMax M2.5
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogMiniMax M2.5
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
MiniMax M2.5
Non-Reasoning
Weight access
GPT-5.4 nano
Proprietary
MiniMax M2.5
Proprietary
License
GPT-5.4 nano
Proprietary
MiniMax M2.5
Proprietary
Release date
GPT-5.4 nano
2026-03-17
MiniMax M2.5
2026-02-12
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
- MiniMax M2.5 has the higher public score estimate, 49.17 versus 46.15, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.01375 vs $0.0186. Cache-heavy agent loop: $0.0205 vs $0.078.
- Context tradeoff
- GPT-5.4 nano has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.4 nano or MiniMax M2.5?
MiniMax M2.5 has the higher public score estimate, 49.17 versus 46.15, 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-5.4 nano or MiniMax M2.5?
MiniMax M2.5 scores higher for coding on the public lane, 34.5 to 30.1. MiniMax M2.5 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-5.4 nano or MiniMax M2.5?
GPT-5.4 nano scores higher for agentic tasks on the public lane, 33.7 to 30.1. MiniMax M2.5 is 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, GPT-5.4 nano or MiniMax M2.5?
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0009 on MiniMax M2.5; repository review costs $0.01375 and $0.0186; the cache-heavy agent loop costs $0.0205 and $0.078. MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.4 nano or MiniMax M2.5?
GPT-5.4 nano has the larger documented context window: 400K, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence23 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceMiniMax M2.5—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceMiniMax M2.5—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%SourceMiniMax M2.5—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%SourceMiniMax M2.5—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceMiniMax M2.5—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%SourceMiniMax M2.5—Not directly comparable
Coding
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceMiniMax M2.5—Not directly comparable
Vibe Code Bench
Shared sourceGPT-5.4 nano26.10%MiniMax M2.514.85%GPT-5.4 nano leads this result
LiveCodeBench (Vals)
GPT-5.4 nano84.0%SourceMiniMax M2.5—Not directly comparable
SWE-bench (Vals)
GPT-5.4 nano69.8%SourceMiniMax M2.5—Not directly comparable
Reasoning
Multimodal
ExtractBench (LlamaIndex)
GPT-5.4 nano74.90%SourceMiniMax M2.5—Not directly comparable
ParseBench
GPT-5.4 nano43.35%SourceMiniMax M2.5—Not directly comparable
MMMU-Pro
GPT-5.4 nano66.1%SourceMiniMax M2.5—Not directly comparable
MMMU-Pro w/ Python
GPT-5.4 nano69.5%SourceMiniMax M2.5—Not directly comparable
Knowledge
GPQA
GPT-5.4 nano82.8%SourceMiniMax M2.5—Not directly comparable
HLE
GPT-5.4 nano37.7%SourceMiniMax M2.5—Not directly comparable
HLE w/o tools
GPT-5.4 nano24.3%SourceMiniMax M2.5—Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 nano77.5%SourceMiniMax M2.5—Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 nano77.2%SourceMiniMax M2.5—Not directly comparable
Math
23 public results · 1 shared
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Last updated October 29, 2026