Agentic
Not comparable- GPT-4.1
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
GPT-4.1 vs MiniMax M2.5
Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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 based on different benchmark sets are marked directional and do not name a winner.
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.
Long documents
Prompts that approach the documented context limit
GPT-4.1
GPT-4.1 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M2.5
MiniMax M2.5 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
MiniMax M2.5
MiniMax M2.5 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Cache-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. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 GPT-4.1 only
1 MiniMax M2.5 only
- Shared results
- 0
- GPT-4.1 only
- 7
- MiniMax M2.5 only
- 1
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- GPT-4.1
- 54.6
- MiniMax M2.5
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4.1
- 66.3
- MiniMax M2.5
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1
- 4.1
- MiniMax M2.5
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1
- Not measured
- MiniMax M2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1
- 87.4
- MiniMax M2.5
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | GPT-4.1 | MiniMax M2.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | 54.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 66.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 4.1 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 87.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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.
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
- MiniMax M2.5
- $0.0009
- Fits in one request
MiniMax M2.5 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
- MiniMax M2.5
- $0.0186
- Fits in one request
MiniMax M2.5 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
- 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. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.5 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-4.1MiniMax M2.5
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1
1M
MiniMax M2.5
128K
API model ID
GPT-4.1
Not sourced
MiniMax 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-4.1
Not published
MiniMax M2.5
Not published
Documented inputs
GPT-4.1
Not sourced
MiniMax M2.5
Not sourced
Documented outputs
GPT-4.1
Not sourced
MiniMax M2.5
Not sourced
Provider availability
GPT-4.1
Not sourced
MiniMax M2.5
Not sourced
Reasoning profile
GPT-4.1
Non-Reasoning
MiniMax M2.5
Non-Reasoning
Weight access
GPT-4.1
Proprietary
MiniMax M2.5
Proprietary
License
GPT-4.1
Proprietary
MiniMax M2.5
Proprietary
Release date
GPT-4.1
2025-04-14
MiniMax M2.5
2025-10-01
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
- Repository review: $0.124 vs $0.0186. Cache-heavy agent loop: $0.52 vs $0.078.
- Context tradeoff
- GPT-4.1 has the larger documented window (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.
Frequently asked questions
Which is better, GPT-4.1 or MiniMax M2.5?
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 or MiniMax M2.5?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-4.1 or MiniMax M2.5?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-4.1 or MiniMax M2.5?
For the stated presets, chat costs $0.006 on GPT-4.1 and $0.0009 on MiniMax M2.5; repository review costs $0.124 and $0.0186; the cache-heavy agent loop costs $0.52 and $0.078. MiniMax M2.5 does not fit this workload in one request. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 or MiniMax M2.5?
GPT-4.1 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated July 29, 2026
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