Updated November 11, 2026. Rank says Qwen3.5-122B-A10B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Qwen3.5-122B-A10B has the higher public point estimate, 52.64 versus 48.59. Their conditional score ranges overlap. These ranges do not establish rank confidence. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
GLM-4.7: No comparable API price
Qwen3.5-122B-A10B: No comparable API price
GLM-4.7: 200K
Qwen3.5-122B-A10B: 262K
GLM-4.7: December 22, 2025
Qwen3.5-122B-A10B: March 4, 2026
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
GLM-4.7
GLM-4.7 has the higher public coding point estimate, 37.8 to 35.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.7 and Qwen3.5-122B-A10B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.
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
GLM-4.7 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 categories rest 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.
BrowseCompAgentic
Normalized gap 11.8Terminal-Bench 2.0Agentic
Normalized gap 8.4MMLU-ProKnowledge
Normalized gap 2.4SWE-bench VerifiedCoding
Normalized gap 1.8GPQAKnowledge
Normalized gap 0.9Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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 | GLM-4.7 | Qwen3.5-122B-A10B | Basis | Reading |
|---|---|---|---|---|
| Coding | 37.8Supported · #76/158 | 35.4Supported · #87/158 | Like-for-likeBenchAlign v5.8 lane · 6 vs 3 public rows | GLM-4.7 leads · intervals overlap |
| Agentic | 34.0Estimated · #94/132 | 33.4Estimated · #95/132 | Directional onlyBenchAlign v5.8 lane · 4 vs 3 public rows | Directional only |
| Reasoning | 36.5Estimated · #100/158 | 40.2Supported · #81/158 | Directional onlyBenchAlign v5.8 lane · 0 vs 1 public rows | Directional only |
| Knowledge | 43.2Supported · #104/179 | 48.0Estimated · #89/179 | Directional onlyBenchAlign v5.8 lane · 5 vs 3 public rows | Directional only |
| Instruction following | 82.8#58/157 | 93.0#13/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multimodal | Not ranked | 51.4Estimated · #52/67 | Not comparableBenchAlign v5.8 lane · 0 vs 6 public rows | Not comparable |
| Multilingual | Not ranked | 87.1#9/17 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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
GLM-4.7 has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.
50K fresh input + 3K output tokens
GLM-4.7 has no comparable published API token rate. Qwen3.5-122B-A10B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate. Qwen3.5-122B-A10B 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.
GLM-4.7
200K
Qwen3.5-122B-A10B
262K
GLM-4.7
Not sourced
Qwen3.5-122B-A10B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-4.7
No comparable hosted API rate
Qwen3.5-122B-A10B
No comparable hosted API rate
GLM-4.7
Qwen3.5-122B-A10B
text, image, video
Qwen3.5-122B-A10B model cardGLM-4.7
Qwen3.5-122B-A10B
GLM-4.7
Not sourced
Qwen3.5-122B-A10B
Not sourced
GLM-4.7
Reasoning
Qwen3.5-122B-A10B
Reasoning
GLM-4.7
Open Weight
Qwen3.5-122B-A10B
Open Weight
GLM-4.7
Open Weight
Qwen3.5-122B-A10B
Open Weight
GLM-4.7
2025-12-22
Qwen3.5-122B-A10B
2026-03-04
Qwen3.5-122B-A10B has the higher public point estimate, 52.64 versus 48.59. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GLM-4.7 has the higher public coding point estimate, 37.8 to 35.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-4.7 scores higher for agentic tasks on the public lane, 34 to 33.4. GLM-4.7 and Qwen3.5-122B-A10B are 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Qwen3.5-122B-A10B has the larger documented context window: 262K, compared with 200K.
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
Qwen3.5-122B-A10B leads this result
BrowseComp
Qwen3.5-122B-A10B leads this result
VITA-Bench
Not directly comparable
Gert Labs
Not directly comparable
OSWorld-Verified
Not directly comparable
SWE-bench Verified
GLM-4.7 leads this result
LiveCodeBench v6
GLM-4.7 leads this result
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Qwen3.5-122B-A10B leads this result
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
LongBench v2
Not directly comparable
MMMU
Not directly comparable
MMVU
Not directly comparable
MMMU-Pro
Not directly comparable
MathVision
Not directly comparable
CharXiv
Not directly comparable
V*
Not directly comparable
GPQA
Qwen3.5-122B-A10B leads this result
MMLU-Pro
Qwen3.5-122B-A10B leads this result
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
SuperGPQA
Not directly comparable
MMLU-ProX
Not directly comparable
IFEval
Not directly comparable
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Last updated November 11, 2026
