Coding
Directional only- GPT-4.1 mini
- 23.6
- MiniMax M3
- 72.2
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
GPT-4.1 mini vs MiniMax M3
Updated August 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
MiniMax M3 has the higher public score, 68.73 versus 44.33, and the 90% score intervals do not overlap.
1 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.
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M3
MiniMax M3 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
MiniMax M3
MiniMax M3 has the lower estimated token cost for this stated workload. GPT-4.1 mini 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
MiniMax M3
MiniMax M3 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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
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.
4 GPT-4.1 mini only
1 Shared
21 MiniMax M3 only
- Shared results
- 1
- GPT-4.1 mini only
- 4
- MiniMax M3 only
- 21
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Not comparable- GPT-4.1 mini
- Not measured
- MiniMax M3
- 72.3
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1 mini
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4.1 mini
- 64.2
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 mini
- 4.5
- MiniMax M3
- 85.7
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 mini
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 mini
- Not measured
- MiniMax M3
- 64.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-4.1 mini
- 88.5
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | GPT-4.1 mini | MiniMax M3 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 23.6 | 72.2 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 72.3 | Not comparable0 vs 3 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 64.2 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 4.5 | 85.7 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 64.9 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | 88.5 | 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.
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.
SWE-bench Verified
Coding
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 mini
- $0.0012
- Fits in one request
- MiniMax M3
- $0.0009
- Fits in one request
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 mini
- $0.0248
- Fits in one request
- MiniMax M3
- $0.0186
- Fits in one request
MiniMax M3 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 mini
- $0.104
- Fits in one request
- Cached input priced at the published list-input rate
- MiniMax M3
- $0.03
- Fits in one request
MiniMax M3 has the lower modeled cost
GPT-4.1 mini 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-4.1 miniMiniMax M3
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 mini
1M
MiniMax M3
1M
API model ID
GPT-4.1 mini
Not sourced
MiniMax M3
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 mini
Not published
MiniMax M3
$0.06 per 1M cached input tokens
Documented inputs
GPT-4.1 mini
Not sourced
MiniMax M3
Not sourced
Documented outputs
GPT-4.1 mini
Not sourced
MiniMax M3
Not sourced
Provider availability
GPT-4.1 mini
Not sourced
MiniMax M3
Not sourced
Reasoning profile
GPT-4.1 mini
Non-Reasoning
MiniMax M3
Non-Reasoning
Weight access
GPT-4.1 mini
Proprietary
MiniMax M3
Open Weight
License
GPT-4.1 mini
Proprietary
MiniMax M3
Open Weight
Release date
GPT-4.1 mini
2025-04-14
MiniMax M3
2026-06-01
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
- MiniMax M3 has the higher public score, 68.73 versus 44.33, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0248 vs $0.0186. Cache-heavy agent loop: $0.104 vs $0.03.
- 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 evidence26 rows
Agentic
Terminal-Bench 2.0
GPT-4.1 mini—MiniMax M366%SourceNot directly comparable
BrowseComp
GPT-4.1 mini—MiniMax M383.5%SourceNot directly comparable
OSWorld-Verified
GPT-4.1 mini—MiniMax M370.1%SourceNot directly comparable
MCP Atlas
GPT-4.1 mini—MiniMax M374.2%SourceNot directly comparable
Claw-Eval
GPT-4.1 mini—MiniMax M374.5%SourceNot directly comparable
BankerToolBench
GPT-4.1 mini—MiniMax M376.1%SourceNot directly comparable
ResearchClawBench
GPT-4.1 mini—MiniMax M319.8%SourceNot directly comparable
OSWorld 2.0
GPT-4.1 mini—MiniMax M34.6%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-4.1 mini23.6%SourceMiniMax M380.5%SourceMiniMax M3 leads this result
SWE-bench Pro
GPT-4.1 mini—MiniMax M359%SourceNot directly comparable
Terminal-Bench 2.0
GPT-4.1 mini—MiniMax M366.0%SourceNot directly comparable
NL2Repo
GPT-4.1 mini—MiniMax M342.1%SourceNot directly comparable
VIBE V2
GPT-4.1 mini—MiniMax M350.1%SourceNot directly comparable
SVG-Bench
GPT-4.1 mini—MiniMax M363.7%SourceNot directly comparable
KernelBench Hard
GPT-4.1 mini—MiniMax M328.8%SourceNot directly comparable
OpenHarmony Bench
GPT-4.1 mini—MiniMax M348.4%SourceNot directly comparable
Knowledge
Math
Multimodal
OfficeQA Pro
GPT-4.1 mini—MiniMax M345.1%SourceNot directly comparable
OmniDocBench 1.5
GPT-4.1 mini—MiniMax M391.6%SourceNot directly comparable
MMMU-Pro
GPT-4.1 mini—MiniMax M378.1%SourceNot directly comparable
VideoMMMU
GPT-4.1 mini—MiniMax M384.6%SourceNot directly comparable
Video-MME (with subtitle)
GPT-4.1 mini—MiniMax M385.4%SourceNot directly comparable
Instruction following
IFEval
GPT-4.1 mini88.5%SourceMiniMax M3—Not directly comparable
Frequently asked questions
Which is better, GPT-4.1 mini or MiniMax M3?
MiniMax M3 has the higher public score, 68.73 versus 44.33, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-4.1 mini or MiniMax M3?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-4.1 mini or MiniMax M3?
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 mini or MiniMax M3?
For the stated presets, chat costs $0.0012 on GPT-4.1 mini and $0.0009 on MiniMax M3; repository review costs $0.0248 and $0.0186; the cache-heavy agent loop costs $0.104 and $0.03. GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 mini or MiniMax M3?
Both models list the same context window, 1M.
Related comparisons
Last updated August 30, 2026
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