Agentic
Directional only- Claude Sonnet 4.5
- 55.4
- MiniMax M2.7
- 57.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
Claude Sonnet 4.5 vs MiniMax M2.7
Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
20 confirmed releases in the last 30 daysSee provider release alerts
MiniMax M2.7 has the higher public score estimate, 63.09 versus 52.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 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 M2.7
MiniMax M2.7 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 M2.7
MiniMax M2.7 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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. Claude Sonnet 4.5 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 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.
9 Claude Sonnet 4.5 only
2 Shared
16 MiniMax M2.7 only
- Shared results
- 2
- Claude Sonnet 4.5 only
- 9
- MiniMax M2.7 only
- 16
- 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.
Coding
Not comparable- Claude Sonnet 4.5
- 77.2
- MiniMax M2.7
- 53.3
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Sonnet 4.5
- 13.6
- MiniMax M2.7
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Sonnet 4.5
- 83.4
- MiniMax M2.7
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 4.5
- 11.2
- MiniMax M2.7
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 4.5
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 4.5
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 4.5
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Sonnet 4.5 | MiniMax M2.7 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 55.4 | 57.0 | Directional only2 vs 1 rows | Directional only |
| Coding | 77.2 | 53.3 | Not comparable1 vs 2 rows | Not comparable |
| Reasoning | 13.6 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 83.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 11.2 | 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 | 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
Claude Sonnet 4.5: 50%MiniMax M2.7: 57%Normalized gap 7.0Claude Sonnet 4.5 source MiniMax M2.7 source
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
- Claude Sonnet 4.5
- $0.0105
- Fits in one request
- MiniMax M2.7
- $0.0009
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 4.5
- $0.195
- Fits in one request
- MiniMax M2.7
- $0.0186
- Fits in one request
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Sonnet 4.5
- $0.81
- Does not fit in one request
- Cached input priced at the published list-input rate
- MiniMax M2.7
- $0.078
- Does not fit in one request
- Cached input priced at the published list-input rate
Claude Sonnet 4.5 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate. 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.
SpecificationClaude Sonnet 4.5MiniMax M2.7
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 4.5
200K
MiniMax M2.7
200K
API model ID
Claude Sonnet 4.5
Not sourced
MiniMax M2.7
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.5
Not published
MiniMax M2.7
Not published
Documented inputs
Claude Sonnet 4.5
Not sourced
MiniMax M2.7
Not sourced
Documented outputs
Claude Sonnet 4.5
Not sourced
MiniMax M2.7
Not sourced
Provider availability
Claude Sonnet 4.5
Not sourced
MiniMax M2.7
Not sourced
Reasoning profile
Claude Sonnet 4.5
Non-Reasoning
MiniMax M2.7
Non-Reasoning
Weight access
Claude Sonnet 4.5
Proprietary
MiniMax M2.7
Open Weight
License
Claude Sonnet 4.5
Proprietary
MiniMax M2.7
Open Weight
Release date
Claude Sonnet 4.5
2025-09-01
MiniMax M2.7
2026-03-18
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 M2.7 has the higher public score estimate, 63.09 versus 52.86, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.195 vs $0.0186. Cache-heavy agent loop: $0.81 vs $0.078.
- Context tradeoff
- Both models list 200K.
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 evidence27 rows
Agentic
Terminal-Bench 2.0
Claude Sonnet 4.550%SourceMiniMax M2.757%SourceMiniMax M2.7 leads this result
OSWorld-Verified
Claude Sonnet 4.561.4%SourceMiniMax M2.7—Not directly comparable
VITA-Bench
Claude Sonnet 4.517.0%SourceMiniMax M2.7—Not directly comparable
Gert Labs
Shared sourceClaude Sonnet 4.548.51%MiniMax M2.740.40%Claude Sonnet 4.5 leads this result
JobBench
Claude Sonnet 4.527.7%SourceMiniMax M2.7—Not directly comparable
Toolathlon
Claude Sonnet 4.5—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
Claude Sonnet 4.5—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
Claude Sonnet 4.5—MiniMax M2.762.7%SourceNot directly comparable
Claw-Eval
Claude Sonnet 4.5—MiniMax M2.748.7%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 4.577.2%SourceMiniMax M2.7—Not directly comparable
SWE-bench Verified*
Claude Sonnet 4.5—MiniMax M2.775.4%SourceNot directly comparable
SWE-bench Pro
Claude Sonnet 4.5—MiniMax M2.756.2%SourceNot directly comparable
SWE-Rebench
Claude Sonnet 4.5—MiniMax M2.751.9%SourceNot directly comparable
SWE Multilingual
Claude Sonnet 4.5—MiniMax M2.776.5%SourceNot directly comparable
Multi-SWE Bench
Claude Sonnet 4.5—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
Claude Sonnet 4.5—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
Claude Sonnet 4.5—MiniMax M2.739.8%SourceNot directly comparable
Vibe Code Bench
Claude Sonnet 4.5—MiniMax M2.727.04%SourceNot directly comparable
React Native Evals
Claude Sonnet 4.5—MiniMax M2.771.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
Claude Sonnet 4.513.6%SourceMiniMax M2.7—Not directly comparable
Knowledge
Math
AIME 2025
Claude Sonnet 4.587%SourceMiniMax M2.7—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Sonnet 4.513.495%SourceMiniMax M2.7—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Sonnet 4.54.167%SourceMiniMax M2.7—Not directly comparable
AIME25 (Arcee)
Claude Sonnet 4.5—MiniMax M2.780.0%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Sonnet 4.5 or MiniMax M2.7?
MiniMax M2.7 has the higher public score estimate, 63.09 versus 52.86, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Sonnet 4.5 or MiniMax M2.7?
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, Claude Sonnet 4.5 or MiniMax M2.7?
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, Claude Sonnet 4.5 or MiniMax M2.7?
For the stated presets, chat costs $0.0105 on Claude Sonnet 4.5 and $0.0009 on MiniMax M2.7; repository review costs $0.195 and $0.0186; the cache-heavy agent loop costs $0.81 and $0.078. Claude Sonnet 4.5 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Sonnet 4.5 or MiniMax M2.7?
Both models list the same context window, 200K.
Related comparisons
Last updated August 1, 2026
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