Updated October 2, 2026. Rank says Qwen3.7 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Qwen3.7 Max has the higher public score estimate, 63.46 versus 52.65, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 11 results are shared. Category rows resting on Estimated evidence or 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
Qwen3.7 Max
Qwen3.7 Max has the higher public agentic point estimate, 39.3 to 36.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Code generation, repair, and software-engineering tasks
No clear pick
The like-for-like coding result is a practical tie on the public lane (within 0.5 points).
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
MiMo-V2.5-Pro has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
HLEKnowledge
Normalized gap 6.6LiveCodeBench (Vals)Coding
Normalized gap 5.7MMLU-Pro (Vals)Knowledge
Normalized gap 4.7SWE-bench ProCoding
Normalized gap 3.4Terminal-Bench 2.0Agentic
Normalized gap 1.3Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | MiMo-V2.5-Pro | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Agentic | 36.9Supported · #65/119 | 39.3Supported · #60/119 | Like-for-likeBenchAlign v5.8 lane · 5 vs 10 public rows | Qwen3.7 Max leads · intervals overlap |
| Coding | 45.7Supported · #52/144 | 45.4Supported · #54/144 | Like-for-likeBenchAlign v5.8 lane · 4 vs 10 public rows | Practical tie |
| Knowledge | 50.7Supported · #62/171 | 59.8Supported · #41/171 | Like-for-likeBenchAlign v5.8 lane · 4 vs 9 public rows | Qwen3.7 Max leads · intervals overlap |
| Instruction following | 92.4#6/125 | 89.2#17/125 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 77.1Unranked · 2 rankable rows | 76.3Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 100.0#1/16 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 81.9Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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
MiMo-V2.5-Pro has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
MiMo-V2.5-Pro has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.5-Pro has no comparable published API token rate. Qwen3.7 Max 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.
MiMo-V2.5-Pro
1M
Qwen3.7 Max
1M
MiMo-V2.5-Pro
Not sourced
Qwen3.7 Max
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiMo-V2.5-Pro
No comparable hosted API rate
Qwen3.7 Max
No comparable hosted API rate
MiMo-V2.5-Pro
Not sourced
Qwen3.7 Max
Not sourced
MiMo-V2.5-Pro
Not sourced
Qwen3.7 Max
Not sourced
MiMo-V2.5-Pro
Not sourced
Qwen3.7 Max
Not sourced
MiMo-V2.5-Pro
Reasoning
Qwen3.7 Max
Reasoning
MiMo-V2.5-Pro
Proprietary
Qwen3.7 Max
Proprietary
MiMo-V2.5-Pro
Proprietary
Qwen3.7 Max
Proprietary
MiMo-V2.5-Pro
2026-04-22
Qwen3.7 Max
2026-05-16
Qwen3.7 Max has the higher public score estimate, 63.46 versus 52.65, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
The like-for-like coding row is a practical tie on the public lane, 45.7 against 45.4, inside the 0.5-point band BenchLM treats as level.
Qwen3.7 Max has the higher public agentic tasks point estimate, 39.3 to 36.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Qwen3.7 Max leads this result
τ³-bench results
Not directly comparable
Terminal-Bench 2.0
Qwen3.7 Max leads this result
Gert Labs
Shared sourceQwen3.7 Max leads this result
Terminal-Bench 2.1 (Vals)
Qwen3.7 Max leads this result
QwenClawBench
Not directly comparable
BFCL v4
Not directly comparable
MCP Atlas
Not directly comparable
VITA-Bench
Not directly comparable
HLE w/ tools
Not directly comparable
ResearchClawBench
Not directly comparable
SWE-bench Pro
Qwen3.7 Max leads this result
Terminal-Bench 2.0
Qwen3.7 Max leads this result
LiveCodeBench (Vals)
Qwen3.7 Max leads this result
SWE-bench (Vals)
MiMo-V2.5-Pro leads this result
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
SciCode
Not directly comparable
LiveCodeBench
Not directly comparable
OpenHarmony Bench
Not directly comparable
HLE
MiMo-V2.5-Pro leads this result
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Qwen3.7 Max leads this result
MMLU-Pro (Vals)
Qwen3.7 Max leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Redux
Not directly comparable
SuperGPQA
Not directly comparable
MMMLU
Not directly comparable
MMLU-ProX
Not directly comparable
NOVA-63
Not directly comparable
INCLUDE
Not directly comparable
MAXIFE
Not directly comparable
PolyMath
Not directly comparable
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Last updated October 2, 2026
