Agentic
Directional only- MiniMax M2.7
- 57.0
- MiniMax M3
- 72.3
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
MiniMax M2.7 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 estimate, 68.73 versus 63.29, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 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
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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 M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
14 MiniMax M2.7 only
4 Shared
18 MiniMax M3 only
- Shared results
- 4
- MiniMax M2.7 only
- 14
- MiniMax M3 only
- 18
- Like-for-like categories
- 0 / 8
2 categories use 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.
Coding
Directional only- MiniMax M2.7
- 53.3
- MiniMax M3
- 72.2
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- MiniMax M2.7
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- MiniMax M2.7
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M2.7
- Not measured
- MiniMax M3
- 85.7
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M2.7
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M2.7
- Not measured
- MiniMax M3
- 64.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- MiniMax M2.7
- Not measured
- MiniMax M3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | MiniMax M2.7 | MiniMax M3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 57.0 | 72.3 | Directional only1 vs 3 rows | Directional only |
| Coding | 53.3 | 72.2 | Directional only2 vs 2 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | 85.7 | Not comparable0 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 | Not measured | Not measured | Not comparable0 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.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
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
- MiniMax M2.7
- $0.0009
- Fits in one request
- MiniMax M3
- $0.0009
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- MiniMax M2.7
- $0.0186
- Fits in one request
- MiniMax M3
- $0.0186
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
- MiniMax M3
- $0.03
- Fits in one request
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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.
SpecificationMiniMax M2.7MiniMax M3
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M2.7
200K
MiniMax M3
1M
API model ID
MiniMax M2.7
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.
MiniMax M2.7
Not published
MiniMax M3
$0.06 per 1M cached input tokens
Documented inputs
MiniMax M2.7
Not sourced
MiniMax M3
Not sourced
Documented outputs
MiniMax M2.7
Not sourced
MiniMax M3
Not sourced
Provider availability
MiniMax M2.7
Not sourced
MiniMax M3
Not sourced
Reasoning profile
MiniMax M2.7
Non-Reasoning
MiniMax M3
Non-Reasoning
Weight access
MiniMax M2.7
Open Weight
MiniMax M3
Open Weight
License
MiniMax M2.7
Open Weight
MiniMax M3
Open Weight
Release date
MiniMax M2.7
2026-03-18
MiniMax M3
2026-06-01
If you are considering the documented upgrade path
- Deployment change
- Both entries list MiniMax as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- MiniMax M3 has the higher public score estimate, 68.73 versus 63.29, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0186 vs $0.0186. Cache-heavy agent loop: $0.078 vs $0.03.
- Context tradeoff
- MiniMax M3 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.
Browse raw public benchmark evidence36 rows
Agentic
Terminal-Bench 2.0
MiniMax M2.757%SourceMiniMax M366%SourceMiniMax M3 leads this result
Toolathlon
MiniMax M2.746.3%SourceMiniMax M3—Not directly comparable
MLE-Bench Lite
MiniMax M2.766.6%SourceMiniMax M3—Not directly comparable
MM-ClawBench
MiniMax M2.762.7%SourceMiniMax M3—Not directly comparable
Claw-Eval
MiniMax M2.748.7%SourceMiniMax M374.5%SourceMiniMax M3 leads this result
Gert Labs
MiniMax M2.740.40%SourceMiniMax M3—Not directly comparable
BrowseComp
MiniMax M2.7—MiniMax M383.5%SourceNot directly comparable
OSWorld-Verified
MiniMax M2.7—MiniMax M370.1%SourceNot directly comparable
MCP Atlas
MiniMax M2.7—MiniMax M374.2%SourceNot directly comparable
BankerToolBench
MiniMax M2.7—MiniMax M376.1%SourceNot directly comparable
ResearchClawBench
MiniMax M2.7—MiniMax M319.8%SourceNot directly comparable
OSWorld 2.0
MiniMax M2.7—MiniMax M34.6%SourceNot directly comparable
Coding
SWE-bench Verified*
MiniMax M2.775.4%SourceMiniMax M3—Not directly comparable
SWE-bench Pro
MiniMax M2.756.2%SourceMiniMax M359%SourceMiniMax M3 leads this result
SWE-Rebench
MiniMax M2.751.9%SourceMiniMax M3—Not directly comparable
SWE Multilingual
MiniMax M2.776.5%SourceMiniMax M3—Not directly comparable
Multi-SWE Bench
MiniMax M2.752.7%SourceMiniMax M3—Not directly comparable
VIBE-Pro
MiniMax M2.755.6%SourceMiniMax M3—Not directly comparable
NL2Repo
MiniMax M2.739.8%SourceMiniMax M342.1%SourceMiniMax M3 leads this result
Vibe Code Bench
MiniMax M2.727.04%SourceMiniMax M3—Not directly comparable
React Native Evals
MiniMax M2.771.4%SourceMiniMax M3—Not directly comparable
SWE-bench Verified
MiniMax M2.7—MiniMax M380.5%SourceNot directly comparable
Terminal-Bench 2.0
MiniMax M2.7—MiniMax M366.0%SourceNot directly comparable
VIBE V2
MiniMax M2.7—MiniMax M350.1%SourceNot directly comparable
SVG-Bench
MiniMax M2.7—MiniMax M363.7%SourceNot directly comparable
KernelBench Hard
MiniMax M2.7—MiniMax M328.8%SourceNot directly comparable
OpenHarmony Bench
MiniMax M2.7—MiniMax M348.4%SourceNot directly comparable
Knowledge
Math
Multimodal
OfficeQA Pro
MiniMax M2.7—MiniMax M345.1%SourceNot directly comparable
OmniDocBench 1.5
MiniMax M2.7—MiniMax M391.6%SourceNot directly comparable
MMMU-Pro
MiniMax M2.7—MiniMax M378.1%SourceNot directly comparable
VideoMMMU
MiniMax M2.7—MiniMax M384.6%SourceNot directly comparable
Video-MME (with subtitle)
MiniMax M2.7—MiniMax M385.4%SourceNot directly comparable
Frequently asked questions
Which is better, MiniMax M2.7 or MiniMax M3?
MiniMax M3 has the higher public score estimate, 68.73 versus 63.29, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, MiniMax M2.7 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, MiniMax M2.7 or MiniMax M3?
The current agentic tasks 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 costs less, MiniMax M2.7 or MiniMax M3?
For the stated presets, chat costs $0.0009 on MiniMax M2.7 and $0.0009 on MiniMax M3; repository review costs $0.0186 and $0.0186; the cache-heavy agent loop costs $0.078 and $0.03. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, MiniMax M2.7 or MiniMax M3?
MiniMax M3 has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated August 30, 2026
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