Long documents
Prompts that approach the documented context limit
Qwen3.7 Max
Qwen3.7 Max has the larger documented context window.
Confidence: documented
GLM-4.6 vs Qwen3.7 Max
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
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Decision reading
Qwen3.7 Max has the higher public score estimate, 63.46 versus 40.39, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- GLM-4.6 only
- 3
- Qwen3.7 Max only
- 38
- Like-for-like categories
- 1 / 8
Supported: GLM-4.6 and Qwen3.7 MaxHow the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-4.6 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.6 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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. GLM-4.6 does not fit this workload in one request. GLM-4.6 has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.
Confidence: listed-ratesRepository 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
Which one for a specific job
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.
27.8GLM-4.645.4Qwen3.7 Max
Directional only · BenchAlign v5.8
Qwen3.7 Max 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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 GLM-4.6 only
3 Shared
38 Qwen3.7 Max only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
MMLU-Pro (Vals)Knowledge
Normalized gap 7.1GLM-4.6:82.2%Qwen3.7 Max:89.3%LiveCodeBench (Vals)Coding
Normalized gap 6.1GLM-4.6:81.0%Qwen3.7 Max:87.1%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each 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.
Knowledge
Like-for-like- GLM-4.6
- 36.3
- Supported · #111/171
- Qwen3.7 Max
- 59.8
- Supported · #41/171
- Basis
- BenchAlign v5.8 lane · 2 vs 9 public rows
- Reading
- Qwen3.7 Max leads
Coding
Directional only- GLM-4.6
- 27.8
- Estimated · #99/144
- Qwen3.7 Max
- 45.4
- Supported · #54/144
- Basis
- BenchAlign v5.8 lane · 2 vs 10 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-4.6
- 40.7
- #100/125
- Qwen3.7 Max
- 89.2
- #17/125
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GLM-4.6
- Not ranked
- Qwen3.7 Max
- 39.3
- Supported · #60/119
- Basis
- BenchAlign v5.8 lane · 0 vs 10 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-4.6
- 40.2
- Unranked · 2 rankable rows
- Qwen3.7 Max
- 76.3
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-4.6
- Not ranked
- Qwen3.7 Max
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-4.6
- Not ranked
- Qwen3.7 Max
- 100.0
- #1/16
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-4.6
- 27.3
- Unranked · 2 rankable rows
- Qwen3.7 Max
- 81.9
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
| Category | GLM-4.6 | Qwen3.7 Max | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 36.3Supported · #111/171 | 59.8Supported · #41/171 | Like-for-likeBenchAlign v5.8 lane · 2 vs 9 public rows | Qwen3.7 Max leads |
| Coding | 27.8Estimated · #99/144 | 45.4Supported · #54/144 | Directional onlyBenchAlign v5.8 lane · 2 vs 10 public rows | Directional only |
| Instruction following | 40.7#100/125 | 89.2#17/125 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | Not ranked | 39.3Supported · #60/119 | Not comparableBenchAlign v5.8 lane · 0 vs 10 public rows | Not comparable |
| Reasoning | 40.2Unranked · 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 | 27.3Unranked · 2 rankable rows | 81.9Unranked · 3 rankable rows | Not comparableProvisional lane · 2 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.
Supported evidence per lane · bars run 0–100Methodology
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
- GLM-4.6
- API rate not published
- Fits in one request
- Qwen3.7 Max
- API rate not published
- Fits in one request
GLM-4.6 has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-4.6
- API rate not published
- Fits in one request
- Qwen3.7 Max
- API rate not published
- Fits in one request
GLM-4.6 has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-4.6
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
- Qwen3.7 Max
- API rate not published
- Fits in one request
- Cached-input rate unavailable
GLM-4.6 does not fit this workload in one request. GLM-4.6 has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-4.6Qwen3.7 Max
Context window
Maximum documented context; output-token limits may be lower.
GLM-4.6
200K
Qwen3.7 Max
1M
API model ID
GLM-4.6
Not sourced
Qwen3.7 Max
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-4.6
No comparable hosted API rate
Qwen3.7 Max
No comparable hosted API rate
Documented inputs
GLM-4.6
Not sourced
Qwen3.7 Max
Not sourced
Documented outputs
GLM-4.6
Not sourced
Qwen3.7 Max
Not sourced
Provider availability
GLM-4.6
Not sourced
Qwen3.7 Max
Not sourced
Reasoning profile
GLM-4.6
Reasoning
Qwen3.7 Max
Reasoning
Weight access
GLM-4.6
Open Weight
Qwen3.7 Max
Proprietary
License
GLM-4.6
Open Weight
Qwen3.7 Max
Proprietary
Release date
GLM-4.6
2025-09-01
Qwen3.7 Max
2026-05-16
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
- Qwen3.7 Max has the higher public score estimate, 63.46 versus 40.39, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.7 Max has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-4.6 or Qwen3.7 Max?
Qwen3.7 Max has the higher public score estimate, 63.46 versus 40.39, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GLM-4.6 or Qwen3.7 Max?
Qwen3.7 Max scores higher for coding on the public lane, 45.4 to 27.8. GLM-4.6 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, GLM-4.6 or Qwen3.7 Max?
GLM-4.6 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-4.6 or Qwen3.7 Max?
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, GLM-4.6 or Qwen3.7 Max?
Qwen3.7 Max has the larger documented context window: 1M, compared with 200K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence44 rows
Agentic
Terminal-Bench 2.0
GLM-4.6—Qwen3.7 Max69.7%SourceNot directly comparable
QwenClawBench
GLM-4.6—Qwen3.7 Max64.3%SourceNot directly comparable
Claw-Eval
GLM-4.6—Qwen3.7 Max65.2%SourceNot directly comparable
BFCL v4
GLM-4.6—Qwen3.7 Max75.0%SourceNot directly comparable
MCP Atlas
GLM-4.6—Qwen3.7 Max76.4%SourceNot directly comparable
VITA-Bench
GLM-4.6—Qwen3.7 Max47.9%SourceNot directly comparable
HLE w/ tools
GLM-4.6—Qwen3.7 Max53.5%SourceNot directly comparable
Gert Labs
GLM-4.6—Qwen3.7 Max64.27%SourceNot directly comparable
ResearchClawBench
GLM-4.6—Qwen3.7 Max18.7%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GLM-4.6—Qwen3.7 Max61.0%SourceNot directly comparable
Coding
Vibe Code Bench
GLM-4.63.09%SourceQwen3.7 Max—Not directly comparable
LiveCodeBench (Vals)
GLM-4.681.0%SourceQwen3.7 Max87.1%SourceQwen3.7 Max leads this result
SWE-bench Verified
GLM-4.6—Qwen3.7 Max80.4%SourceNot directly comparable
SWE-bench Pro
GLM-4.6—Qwen3.7 Max60.6%SourceNot directly comparable
SWE Multilingual
GLM-4.6—Qwen3.7 Max78.3%SourceNot directly comparable
NL2Repo
GLM-4.6—Qwen3.7 Max47.2%SourceNot directly comparable
SciCode
GLM-4.6—Qwen3.7 Max53.5%SourceNot directly comparable
LiveCodeBench
GLM-4.6—Qwen3.7 Max91.6%SourceNot directly comparable
Terminal-Bench 2.0
GLM-4.6—Qwen3.7 Max69.7%SourceNot directly comparable
OpenHarmony Bench
GLM-4.6—Qwen3.7 Max53.4%SourceNot directly comparable
SWE-bench (Vals)
GLM-4.6—Qwen3.7 Max68.8%SourceNot directly comparable
Reasoning
Knowledge
GPQA Diamond (Vals)
GLM-4.674.5%SourceQwen3.7 Max90.2%SourceQwen3.7 Max leads this result
MMLU-Pro (Vals)
GLM-4.682.2%SourceQwen3.7 Max89.3%SourceQwen3.7 Max leads this result
GPQA
GLM-4.6—Qwen3.7 Max92.4%SourceNot directly comparable
GPQA-D
GLM-4.6—Qwen3.7 Max92.4%SourceNot directly comparable
HLE
GLM-4.6—Qwen3.7 Max41.4%SourceNot directly comparable
MMLU-Pro
GLM-4.6—Qwen3.7 Max89.6%SourceNot directly comparable
MMLU-Redux
GLM-4.6—Qwen3.7 Max95%SourceNot directly comparable
SuperGPQA
GLM-4.6—Qwen3.7 Max73.6%SourceNot directly comparable
MMMLU
GLM-4.6—Qwen3.7 Max90.3%SourceNot directly comparable
Multilingual
MMLU-ProX
GLM-4.6—Qwen3.7 Max87%SourceNot directly comparable
NOVA-63
GLM-4.6—Qwen3.7 Max59.0%SourceNot directly comparable
INCLUDE
GLM-4.6—Qwen3.7 Max86.2%SourceNot directly comparable
MAXIFE
GLM-4.6—Qwen3.7 Max89.2%SourceNot directly comparable
PolyMath
GLM-4.6—Qwen3.7 Max86.5%SourceNot directly comparable
Instruction following
Math
FrontierMath v2 (Tiers 1-3)
GLM-4.63.819%SourceQwen3.7 Max—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-4.62.128%SourceQwen3.7 Max—Not directly comparable
HMMT Feb 2026
GLM-4.6—Qwen3.7 Max97.1%SourceNot directly comparable
IMOAnswerBench
GLM-4.6—Qwen3.7 Max90.0%SourceNot directly comparable
Apex
GLM-4.6—Qwen3.7 Max44.5%SourceNot directly comparable
44 public results · 3 shared
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Last updated October 2, 2026