Agentic
Like-for-like- MiniMax M3
- 72.3
- Qwen3.5-27B
- 52.0
- Weighted basis
- 3 vs 3 rows
- Reading
- MiniMax M3 leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start the free Radar BriefUpdated 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 60.04, 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.
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.
Tool use, computer use, and multi-step task completion
MiniMax M3
MiniMax M3 leads on the same 3 weighted benchmark rows.
Confidence: stronger
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented
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
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | MiniMax M3 | Qwen3.5-27B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 72.3 | 52.0 | Like-for-like3 vs 3 rows | MiniMax M3 leads |
| Coding | 72.2 | 64.9 | Directional only2 vs 2 rows | Directional only |
| Reasoning | Not measured | 60.6 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 82.7 | Not comparable0 vs 3 rows | Not comparable |
| Math | 85.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | 82.2 | Not comparable0 vs 1 rows | Not comparable |
| Multimodal | 64.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | 95.0 | Not comparable0 vs 1 rows | Not comparable |
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
BrowseComp
Agentic
OSWorld-Verified
Agentic
SWE-bench Verified
Coding
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.
1K fresh input + 500 output tokens
Qwen3.5-27B has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.5-27B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Qwen3.5-27B has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
MiniMax M3
1M
Qwen3.5-27B
262K
MiniMax M3
Not sourced
Qwen3.5-27B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiniMax M3
$0.06 per 1M cached input tokens
Qwen3.5-27B
No comparable hosted API rate
MiniMax M3
Not sourced
Qwen3.5-27B
Not sourced
MiniMax M3
Not sourced
Qwen3.5-27B
Not sourced
MiniMax M3
Not sourced
Qwen3.5-27B
Not sourced
MiniMax M3
Non-Reasoning
Qwen3.5-27B
Reasoning
MiniMax M3
Open Weight
Qwen3.5-27B
Open Weight
MiniMax M3
Open Weight
Qwen3.5-27B
Open Weight
MiniMax M3
2026-06-01
Qwen3.5-27B
2026-03-04
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
MiniMax M3 leads this result
BrowseComp
MiniMax M3 leads this result
OSWorld-Verified
MiniMax M3 leads this result
MCP Atlas
Not directly comparable
Claw-Eval
Not directly comparable
BankerToolBench
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
Gert Labs
Not directly comparable
SWE-bench Verified
MiniMax M3 leads this result
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
NL2Repo
Not directly comparable
VIBE V2
Not directly comparable
SVG-Bench
Not directly comparable
KernelBench Hard
Not directly comparable
OpenHarmony Bench
Not directly comparable
SWE-Rebench
Not directly comparable
LongBench v2
Not directly comparable
USAMO 2026
Not directly comparable
MMLU-ProX
Not directly comparable
OfficeQA Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
MMMU-Pro
Not directly comparable
VideoMMMU
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
MMMU
Not directly comparable
MMVU
Not directly comparable
MathVision
Not directly comparable
V*
Not directly comparable
IFEval
Not directly comparable
MiniMax M3 has the higher public score estimate, 68.73 versus 60.04, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
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.
MiniMax M3 leads the like-for-like agentic tasks comparison across 3 shared weighted benchmark rows.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
MiniMax M3 has the larger documented context window: 1M, compared with 262K.
Last updated August 30, 2026
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