Agentic
Directional only- MiniMax M2.7
- 43.1
- Estimated · #107/151
- Qwen3.6-27B
- 33.9
- Supported · #133/151
- Basis
- BenchAlign lane · 7 vs 6 public rows
- Reading
- Directional only
MiniMax M2.7 vs Qwen3.6-27B
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
MiniMax M2.7 has the higher public score estimate, 58.99 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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
Qwen3.6-27B
Qwen3.6-27B 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
MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiniMax M2.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
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
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. Qwen3.6-27B has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
17 MiniMax M2.7 only
6 Shared
32 Qwen3.6-27B only
- Shared results
- 6
- MiniMax M2.7 only
- 17
- Qwen3.6-27B only
- 32
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Directional only- MiniMax M2.7
- 50.5
- Estimated · #70/183
- Qwen3.6-27B
- 42.6
- Supported · #128/183
- Basis
- BenchAlign lane · 11 vs 6 public rows
- Reading
- Directional only
Knowledge
Directional only- MiniMax M2.7
- 50.2
- Supported · #88/181
- Qwen3.6-27B
- 49.0
- Estimated · #95/181
- Basis
- BenchAlign lane · 4 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- MiniMax M2.7
- 92.7
- #10/120
- Qwen3.6-27B
- 82.2
- #50/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- MiniMax M2.7
- 75.2
- Unranked · 2 rankable rows
- Qwen3.6-27B
- 73.7
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiniMax M2.7
- Not ranked
- Qwen3.6-27B
- 72.8
- Unranked · 5 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MiniMax M2.7
- Not ranked
- Qwen3.6-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiniMax M2.7
- Not ranked
- Qwen3.6-27B
- 51.5
- #35/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | MiniMax M2.7 | Qwen3.6-27B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.1Estimated · #107/151 | 33.9Supported · #133/151 | Directional onlyBenchAlign lane · 7 vs 6 public rows | Directional only |
| Coding | 50.5Estimated · #70/183 | 42.6Supported · #128/183 | Directional onlyBenchAlign lane · 11 vs 6 public rows | Directional only |
| Knowledge | 50.2Supported · #88/181 | 49.0Estimated · #95/181 | Directional onlyBenchAlign lane · 4 vs 6 public rows | Directional only |
| Instruction following | 92.7#10/120 | 82.2#50/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 75.2Unranked · 2 rankable rows | 73.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 72.8Unranked · 5 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 51.5#35/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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 Multilingual
Coding
SWE-bench Pro
Coding
Terminal-Bench 2.0
Agentic
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
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.6-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiniMax M2.7
- $0.0186
- Fits in one request
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.6-27B has no comparable published API token rate.
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
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
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. Qwen3.6-27B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMiniMax M2.7Qwen3.6-27B
Context window
Maximum documented context; output-token limits may be lower.
MiniMax M2.7
200K
Qwen3.6-27B
262K
API model ID
MiniMax M2.7
Not sourced
Qwen3.6-27B
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
Qwen3.6-27B
No comparable hosted API rate
Documented inputs
MiniMax M2.7
Not sourced
Qwen3.6-27B
Not sourced
Documented outputs
MiniMax M2.7
Not sourced
Qwen3.6-27B
Not sourced
Provider availability
MiniMax M2.7
Not sourced
Qwen3.6-27B
Not sourced
Reasoning profile
MiniMax M2.7
Non-Reasoning
Qwen3.6-27B
Reasoning
Weight access
MiniMax M2.7
Open Weight
Qwen3.6-27B
Open Weight
License
MiniMax M2.7
Open Weight
Qwen3.6-27B
Open Weight
Release date
MiniMax M2.7
2026-03-18
Qwen3.6-27B
2026-04-21
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, 58.99 versus 52.73, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.6-27B has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
MiniMax M2.7
API / mo$1,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even—
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence55 rows
Agentic
Terminal-Bench 2.0
MiniMax M2.757%SourceQwen3.6-27B59.3%SourceQwen3.6-27B leads this result
Toolathlon
MiniMax M2.746.3%SourceQwen3.6-27B—Not directly comparable
MLE-Bench Lite
MiniMax M2.766.6%SourceQwen3.6-27B—Not directly comparable
MM-ClawBench
MiniMax M2.762.7%SourceQwen3.6-27B—Not directly comparable
Claw-Eval
MiniMax M2.748.7%SourceQwen3.6-27B72.4%SourceQwen3.6-27B leads this result
Gert Labs
Shared sourceMiniMax M2.740.40%Qwen3.6-27B54.84%Qwen3.6-27B leads this result
Terminal-Bench 2.1 (Vals)
MiniMax M2.748.7%SourceQwen3.6-27B—Not directly comparable
QwenClawBench
MiniMax M2.7—Qwen3.6-27B53.4%SourceNot directly comparable
QwenWebBench
MiniMax M2.7—Qwen3.6-27B1487SourceNot directly comparable
AndroidWorld
MiniMax M2.7—Qwen3.6-27B70.3%SourceNot directly comparable
Coding
SWE-bench Verified*
MiniMax M2.775.4%SourceQwen3.6-27B—Not directly comparable
SWE-bench Pro
MiniMax M2.756.2%SourceQwen3.6-27B53.5%SourceMiniMax M2.7 leads this result
SWE-Rebench
MiniMax M2.751.9%SourceQwen3.6-27B—Not directly comparable
SWE Multilingual
MiniMax M2.776.5%SourceQwen3.6-27B71.3%SourceMiniMax M2.7 leads this result
Multi-SWE Bench
MiniMax M2.752.7%SourceQwen3.6-27B—Not directly comparable
VIBE-Pro
MiniMax M2.755.6%SourceQwen3.6-27B—Not directly comparable
NL2Repo
MiniMax M2.739.8%SourceQwen3.6-27B36.2%SourceMiniMax M2.7 leads this result
Vibe Code Bench
MiniMax M2.727.04%SourceQwen3.6-27B—Not directly comparable
React Native Evals
MiniMax M2.771.4%SourceQwen3.6-27B—Not directly comparable
LiveCodeBench (Vals)
MiniMax M2.779.9%SourceQwen3.6-27B—Not directly comparable
SWE-bench (Vals)
MiniMax M2.773.8%SourceQwen3.6-27B—Not directly comparable
SWE-bench Verified
MiniMax M2.7—Qwen3.6-27B77.2%SourceNot directly comparable
Terminal-Bench 2.0
MiniMax M2.7—Qwen3.6-27B59.3%SourceNot directly comparable
LiveCodeBench
MiniMax M2.7—Qwen3.6-27B83.9%SourceNot directly comparable
Knowledge
GPQA-D
MiniMax M2.787.0%SourceQwen3.6-27B—Not directly comparable
MMLU-Pro (Arcee)
MiniMax M2.780.8%SourceQwen3.6-27B—Not directly comparable
GPQA Diamond (Vals)
MiniMax M2.786.6%SourceQwen3.6-27B—Not directly comparable
MMLU-Pro (Vals)
MiniMax M2.780.4%SourceQwen3.6-27B—Not directly comparable
MMLU-Pro
MiniMax M2.7—Qwen3.6-27B86.2%SourceNot directly comparable
MMLU-Redux
MiniMax M2.7—Qwen3.6-27B93.5%SourceNot directly comparable
SuperGPQA
MiniMax M2.7—Qwen3.6-27B66%SourceNot directly comparable
C-Eval
MiniMax M2.7—Qwen3.6-27B91.4%SourceNot directly comparable
GPQA
MiniMax M2.7—Qwen3.6-27B87.8%SourceNot directly comparable
HLE
MiniMax M2.7—Qwen3.6-27B24%SourceNot directly comparable
Math
AIME25 (Arcee)
MiniMax M2.780.0%SourceQwen3.6-27B—Not directly comparable
HMMT Feb 2025
MiniMax M2.7—Qwen3.6-27B93.8%SourceNot directly comparable
HMMT Nov 2025
MiniMax M2.7—Qwen3.6-27B90.7%SourceNot directly comparable
HMMT Feb 2026
MiniMax M2.7—Qwen3.6-27B84.3%SourceNot directly comparable
MMAnswerBench
MiniMax M2.7—Qwen3.6-27B80.8%SourceNot directly comparable
AIME26
MiniMax M2.7—Qwen3.6-27B94.1%SourceNot directly comparable
Multimodal
MMMU
MiniMax M2.7—Qwen3.6-27B82.9%SourceNot directly comparable
MMMU-Pro
MiniMax M2.7—Qwen3.6-27B75.8%SourceNot directly comparable
RealWorldQA
MiniMax M2.7—Qwen3.6-27B84.1%SourceNot directly comparable
DynaMath
MiniMax M2.7—Qwen3.6-27B85.6%SourceNot directly comparable
MStar
MiniMax M2.7—Qwen3.6-27B81.4%SourceNot directly comparable
SimpleVQA
MiniMax M2.7—Qwen3.6-27B56.1%SourceNot directly comparable
CharXiv
MiniMax M2.7—Qwen3.6-27B78.4%SourceNot directly comparable
CC-OCR
MiniMax M2.7—Qwen3.6-27B81.2%SourceNot directly comparable
CountBench
MiniMax M2.7—Qwen3.6-27B97.8%SourceNot directly comparable
RefCOCO (avg)
MiniMax M2.7—Qwen3.6-27B92.5%SourceNot directly comparable
ERQA
MiniMax M2.7—Qwen3.6-27B62.5%SourceNot directly comparable
Video-MME (with subtitle)
MiniMax M2.7—Qwen3.6-27B87.7%SourceNot directly comparable
VideoMMMU
MiniMax M2.7—Qwen3.6-27B84.4%SourceNot directly comparable
MLVU (M-Avg)
MiniMax M2.7—Qwen3.6-27B86.6%SourceNot directly comparable
V*
MiniMax M2.7—Qwen3.6-27B94.7%SourceNot directly comparable
Frequently asked questions
Which is better, MiniMax M2.7 or Qwen3.6-27B?
MiniMax M2.7 has the higher public score estimate, 58.99 versus 52.73, 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 Qwen3.6-27B?
MiniMax M2.7 scores higher for coding on the public lane, 50.5 to 42.6. MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; 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 Qwen3.6-27B?
MiniMax M2.7 scores higher for agentic tasks on the public lane, 43.1 to 33.9. MiniMax M2.7 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, MiniMax M2.7 or Qwen3.6-27B?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, MiniMax M2.7 or Qwen3.6-27B?
Qwen3.6-27B has the larger documented context window: 262K, compared with 200K.
Related comparisons
Last updated September 4, 2026
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