Long documents
Prompts that approach the documented context limit
Qwen3.6 Plus
Qwen3.6 Plus has the larger documented context window.
Confidence: documented
GLM-5 vs Qwen3.6 Plus
Updated October 6, 2026. Rank cannot separate these two. Price, access, and your workload decide. 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
Qwen3.6 Plus has the higher public score estimate, 53.72 versus 53.53, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 29 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 29
- GLM-5 only
- 7
- Qwen3.6 Plus only
- 19
- Like-for-like categories
- 2 / 8
Supported: GLM-5 and Qwen3.6 PlusHow 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-5 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-5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6 Plus has no comparable published API token rate.
Confidence: rate-fallbackRepository 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.
39.0GLM-541.7Qwen3.6 Plus
Directional only · BenchAlign v5.8
Qwen3.6 Plus 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.
7 GLM-5 only
29 Shared
19 Qwen3.6 Plus only
4 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.
HLEKnowledge
Normalized gap 21.6GLM-5:50.4%Qwen3.6 Plus:28.8%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 9.8GLM-5:16.434%Qwen3.6 Plus:26.207%FrontierMath v2 (Tier 4)Math
Normalized gap 6.2GLM-5:2.100%Qwen3.6 Plus:8.333%Terminal-Bench 2.0Agentic
Normalized gap 5.4GLM-5:56.2%Qwen3.6 Plus:61.6%SuperGPQAKnowledge
Normalized gap 4.8GLM-5:66.8%Qwen3.6 Plus:71.6%
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.
Multilingual
Like-for-like- GLM-5
- 89.5
- #6/16
- Qwen3.6 Plus
- 93.8
- #4/16
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Qwen3.6 Plus leads
Math
Like-for-like- GLM-5
- 56.5
- #7/7
- Qwen3.6 Plus
- 62.0
- #5/7
- Basis
- Provisional lane · 4 vs 4 weighted rows
- Reading
- Qwen3.6 Plus leads
Agentic
Directional only- GLM-5
- 39.1
- Estimated · #61/120
- Qwen3.6 Plus
- 33.8
- Supported · #75/120
- Basis
- BenchAlign v5.8 lane · 11 vs 13 public rows
- Reading
- Directional only
Coding
Directional only- GLM-5
- 39.0
- Estimated · #65/144
- Qwen3.6 Plus
- 41.7
- Supported · #59/144
- Basis
- BenchAlign v5.8 lane · 6 vs 7 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5
- 48.4
- Estimated · #75/173
- Qwen3.6 Plus
- 52.5
- Supported · #62/173
- Basis
- BenchAlign v5.8 lane · 6 vs 8 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5
- 87.2
- #31/125
- Qwen3.6 Plus
- 82.5
- #48/125
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5
- 53.3
- Unranked · 4 rankable rows
- Qwen3.6 Plus
- 61.3
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5
- Not ranked
- Qwen3.6 Plus
- 67.2
- #23/49
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | GLM-5 | Qwen3.6 Plus | Basis | Reading |
|---|---|---|---|---|
| Multilingual | 89.5#6/16 | 93.8#4/16 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Qwen3.6 Plus leads |
| Math | 56.5#7/7 | 62.0#5/7 | Like-for-likeProvisional lane · 4 vs 4 weighted rows | Qwen3.6 Plus leads |
| Agentic | 39.1Estimated · #61/120 | 33.8Supported · #75/120 | Directional onlyBenchAlign v5.8 lane · 11 vs 13 public rows | Directional only |
| Coding | 39.0Estimated · #65/144 | 41.7Supported · #59/144 | Directional onlyBenchAlign v5.8 lane · 6 vs 7 public rows | Directional only |
| Knowledge | 48.4Estimated · #75/173 | 52.5Supported · #62/173 | Directional onlyBenchAlign v5.8 lane · 6 vs 8 public rows | Directional only |
| Instruction following | 87.2#31/125 | 82.5#48/125 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 53.3Unranked · 4 rankable rows | 61.3Unranked · 4 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 67.2#23/49 | 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 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-5
- $0.0026
- Fits in one request
- Qwen3.6 Plus
- API rate not published
- Fits in one request
Qwen3.6 Plus has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5
- $0.0596
- Fits in one request
- Qwen3.6 Plus
- API rate not published
- Fits in one request
Qwen3.6 Plus has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5
- $0.252
- Does not fit in one request
- Cached input priced at the published list-input rate
- Qwen3.6 Plus
- API rate not published
- Fits in one request
- Cached-input rate unavailable
GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6 Plus 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-5Qwen3.6 Plus
Context window
Maximum documented context; output-token limits may be lower.
GLM-5
200K
Qwen3.6 Plus
1M
API model ID
GLM-5
Not sourced
Qwen3.6 Plus
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5
Not published
Qwen3.6 Plus
No comparable hosted API rate
Documented inputs
GLM-5
Not sourced
Qwen3.6 Plus
Not sourced
Documented outputs
GLM-5
Not sourced
Qwen3.6 Plus
Not sourced
Provider availability
GLM-5
Not sourced
Qwen3.6 Plus
Not sourced
Reasoning profile
GLM-5
Non-Reasoning
Qwen3.6 Plus
Reasoning
Weight access
GLM-5
Open Weight
Qwen3.6 Plus
Proprietary
License
GLM-5
Open Weight
Qwen3.6 Plus
Proprietary
Release date
GLM-5
2026-03-01
Qwen3.6 Plus
2026-04-02
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
- Qwen3.6 Plus has the higher public score estimate, 53.72 versus 53.53, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.6 Plus has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5 or Qwen3.6 Plus?
Qwen3.6 Plus has the higher public score estimate, 53.72 versus 53.53, 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-5 or Qwen3.6 Plus?
Qwen3.6 Plus scores higher for coding on the public lane, 41.7 to 39. GLM-5 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-5 or Qwen3.6 Plus?
GLM-5 scores higher for agentic tasks on the public lane, 39.1 to 33.8. GLM-5 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, GLM-5 or Qwen3.6 Plus?
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-5 or Qwen3.6 Plus?
Qwen3.6 Plus 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 evidence55 rows
Agentic
Terminal-Bench 2.0
GLM-556.2%SourceQwen3.6 Plus61.6%SourceQwen3.6 Plus leads this result
Claw-Eval
GLM-557.7%SourceQwen3.6 Plus58.8%SourceQwen3.6 Plus leads this result
QwenClawBench
Shared sourceGLM-554.1%Qwen3.6 Plus57.2%Qwen3.6 Plus leads this result
τ³-bench results
Shared sourceGLM-565.6%Qwen3.6 Plus70.7%Qwen3.6 Plus leads this result
DeepPlanning
Shared sourceGLM-514.6%Qwen3.6 Plus41.5%Qwen3.6 Plus leads this result
Toolathlon
Shared sourceGLM-538%Qwen3.6 Plus39.8%Qwen3.6 Plus leads this result
MCP Atlas
Shared sourceGLM-531.1%Qwen3.6 Plus48.2%Qwen3.6 Plus leads this result
MCP-Tasks
Shared sourceGLM-560.8%Qwen3.6 Plus74.1%Qwen3.6 Plus leads this result
WideResearch
Shared sourceGLM-569.8%Qwen3.6 Plus74.3%Qwen3.6 Plus leads this result
CyberGym
GLM-543.2%SourceQwen3.6 Plus—Not directly comparable
Gert Labs
Shared sourceGLM-550.99%Qwen3.6 Plus50.60%GLM-5 leads this result
VITA-Bench
GLM-5—Qwen3.6 Plus44.3%SourceNot directly comparable
ResearchClawBench
GLM-5—Qwen3.6 Plus18.0%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5—Qwen3.6 Plus53.2%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-577.8%SourceQwen3.6 Plus78.8%SourceQwen3.6 Plus leads this result
SWE-bench Verified*
GLM-572.8%SourceQwen3.6 Plus—Not directly comparable
SWE-bench Pro
Shared sourceGLM-555.1%Qwen3.6 Plus56.6%Qwen3.6 Plus leads this result
SWE Multilingual
Shared sourceGLM-573.3%Qwen3.6 Plus73.8%Qwen3.6 Plus leads this result
SWE-Rebench
GLM-562.8%SourceQwen3.6 Plus—Not directly comparable
React Native Evals
GLM-574.8%SourceQwen3.6 Plus—Not directly comparable
LiveCodeBench v6
GLM-5—Qwen3.6 Plus87.1%SourceNot directly comparable
Vibe Code Bench
GLM-5—Qwen3.6 Plus25.56%SourceNot directly comparable
LiveCodeBench (Vals)
GLM-5—Qwen3.6 Plus86.0%SourceNot directly comparable
SWE-bench (Vals)
GLM-5—Qwen3.6 Plus73.4%SourceNot directly comparable
Reasoning
LongBench v2
Shared sourceGLM-560.8%Qwen3.6 Plus62%Qwen3.6 Plus leads this result
AI-Needle
Shared sourceGLM-563.3%Qwen3.6 Plus68.3%Qwen3.6 Plus leads this result
Multimodal
MMMU
GLM-5—Qwen3.6 Plus86.0%SourceNot directly comparable
MMMU-Pro
GLM-5—Qwen3.6 Plus78.8%SourceNot directly comparable
MathVision
GLM-5—Qwen3.6 Plus88.0%SourceNot directly comparable
VideoMMMU
GLM-5—Qwen3.6 Plus84.0%SourceNot directly comparable
ScreenSpot Pro
GLM-5—Qwen3.6 Plus68.2%SourceNot directly comparable
CharXiv
GLM-5—Qwen3.6 Plus81.5%SourceNot directly comparable
V*
GLM-5—Qwen3.6 Plus96.9%SourceNot directly comparable
Knowledge
GPQA
Shared sourceGLM-586%Qwen3.6 Plus90.4%Qwen3.6 Plus leads this result
GPQA-D
GLM-586.0%SourceQwen3.6 Plus—Not directly comparable
SuperGPQA
Shared sourceGLM-566.8%Qwen3.6 Plus71.6%Qwen3.6 Plus leads this result
MMLU-Pro
Shared sourceGLM-585.7%Qwen3.6 Plus88.5%Qwen3.6 Plus leads this result
MMLU-Pro (Arcee)
GLM-585.8%SourceQwen3.6 Plus—Not directly comparable
- GLM-550.4%Qwen3.6 Plus28.8%
GLM-5 leads this result
MMLU-Redux
GLM-5—Qwen3.6 Plus94.5%SourceNot directly comparable
C-Eval
GLM-5—Qwen3.6 Plus93.3%SourceNot directly comparable
GPQA Diamond (Vals)
GLM-5—Qwen3.6 Plus87.4%SourceNot directly comparable
MMLU-Pro (Vals)
GLM-5—Qwen3.6 Plus87.7%SourceNot directly comparable
Multilingual
MMLU-ProX
Shared sourceGLM-583.1%Qwen3.6 Plus84.7%Qwen3.6 Plus leads this result
NOVA-63
Shared sourceGLM-555.1%Qwen3.6 Plus57.9%Qwen3.6 Plus leads this result
Instruction following
IFEval
Shared sourceGLM-592.6%Qwen3.6 Plus94.3%Qwen3.6 Plus leads this result
IFBench
GLM-5—Qwen3.6 Plus75.8%SourceNot directly comparable
Math
AIME26
Shared sourceGLM-595.8%Qwen3.6 Plus95.3%GLM-5 leads this result
AIME25 (Arcee)
GLM-593.3%SourceQwen3.6 Plus—Not directly comparable
HMMT Feb 2025
Shared sourceGLM-597.5%Qwen3.6 Plus96.7%GLM-5 leads this result
HMMT Nov 2025
Shared sourceGLM-596.9%Qwen3.6 Plus94.6%GLM-5 leads this result
HMMT Feb 2026
Shared sourceGLM-586.4%Qwen3.6 Plus87.8%Qwen3.6 Plus leads this result
MMAnswerBench
Shared sourceGLM-582.5%Qwen3.6 Plus83.8%Qwen3.6 Plus leads this result
FrontierMath v2 (Tiers 1-3)
Shared sourceGLM-516.434%Qwen3.6 Plus26.207%Qwen3.6 Plus leads this result
FrontierMath v2 (Tier 4)
Shared sourceGLM-52.100%Qwen3.6 Plus8.333%Qwen3.6 Plus leads this result
55 public results · 29 shared
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Last updated October 6, 2026