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 point estimate, 63.46 versus 60.2. Their conditional score ranges overlap. These ranges do not establish rank confidence. 19 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.
Code generation, repair, and software-engineering tasks
Kimi K2.6
Kimi K2.6 has the higher public coding point estimate, 46 to 45.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Kimi K2.6
Kimi K2.6 has the higher public agentic point estimate, 43.4 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Qwen3.7 Max
Qwen3.7 Max has the larger documented context window.
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
Kimi K2.6 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.
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 6.7HMMT Feb 2026Math
Normalized gap 4.4Terminal-Bench 2.0Agentic
Normalized gap 3.0SWE-bench ProCoding
Normalized gap 2.0GPQAKnowledge
Normalized gap 1.9Each 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 | Kimi K2.6 | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.4Supported · #54/119 | 39.3Supported · #60/119 | Like-for-likeBenchAlign v5.8 lane · 12 vs 10 public rows | Kimi K2.6 leads · intervals overlap |
| Coding | 46.0Supported · #51/144 | 45.4Supported · #54/144 | Like-for-likeBenchAlign v5.8 lane · 10 vs 10 public rows | Kimi K2.6 leads · intervals overlap |
| Knowledge | 58.3Supported · #46/171 | 59.8Supported · #41/171 | Like-for-likeBenchAlign v5.8 lane · 5 vs 9 public rows | Qwen3.7 Max leads · intervals overlap |
| Reasoning | Not ranked | 76.3Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 64.9#26/49 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 100.0#1/16 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 89.2#17/125 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 71.0#1/7 | 81.9Unranked · 3 rankable rows | Not comparableProvisional lane · 4 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
Qwen3.7 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.7 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
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.
Kimi K2.6
256K
Qwen3.7 Max
1M
Kimi K2.6
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.
Kimi K2.6
$0.16 per 1M cached input tokens
Qwen3.7 Max
No comparable hosted API rate
Kimi K2.6
Not sourced
Qwen3.7 Max
Not sourced
Kimi K2.6
Not sourced
Qwen3.7 Max
Not sourced
Kimi K2.6
Not sourced
Qwen3.7 Max
Not sourced
Kimi K2.6
Reasoning
Qwen3.7 Max
Reasoning
Kimi K2.6
Open Weight
Qwen3.7 Max
Proprietary
Kimi K2.6
Open Weight
Qwen3.7 Max
Proprietary
Kimi K2.6
2026-04-20
Qwen3.7 Max
2026-05-16
Qwen3.7 Max has the higher public point estimate, 63.46 versus 60.2. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Kimi K2.6 has the higher public coding point estimate, 46 to 45.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Kimi K2.6 has the higher public agentic tasks point estimate, 43.4 to 39.3, 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.
Qwen3.7 Max has the larger documented context window: 1M, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Qwen3.7 Max leads this result
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Qwen3.7 Max leads this result
Claw-Eval
Qwen3.7 Max leads this result
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Shared sourceQwen3.7 Max leads this result
ResearchClawBench
Shared sourceQwen3.7 Max leads this result
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.7 Max leads this result
QwenClawBench
Not directly comparable
BFCL v4
Not directly comparable
VITA-Bench
Not directly comparable
HLE w/ tools
Not directly comparable
SWE-bench Verified
Qwen3.7 Max leads this result
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Qwen3.7 Max leads this result
SWE Multilingual
Qwen3.7 Max leads this result
SciCode
Qwen3.7 Max leads this result
Terminal-Bench 2.0
Qwen3.7 Max leads this result
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
LiveCodeBench (Vals)
Qwen3.7 Max leads this result
SWE-bench (Vals)
Kimi K2.6 leads this result
NL2Repo
Not directly comparable
LiveCodeBench
Not directly comparable
OpenHarmony Bench
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
V*
Not directly comparable
GPQA
Qwen3.7 Max leads this result
GPQA-D
Qwen3.7 Max leads this result
HLE
Qwen3.7 Max leads this result
GPQA Diamond (Vals)
Qwen3.7 Max leads this result
MMLU-Pro (Vals)
Qwen3.7 Max leads this result
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
AIME26
Not directly comparable
HMMT Feb 2026
Qwen3.7 Max leads this result
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated October 2, 2026
