Agentic
Directional only- Composer 2
- 61.7
- GLM-4.7
- 45.7
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.
See RadarModel comparison
Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 results are shared. Category rows based on 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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. Composer 2 does not fit this workload in one request. GLM-4.7 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 has no comparable published API token rate.
Confidence: rate-fallback
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
| Category | Composer 2 | GLM-4.7 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 61.7 | 45.7 | Directional only1 vs 2 rows | Directional only |
| Coding | 58.0 | 75.4 | Directional only1 vs 3 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 51.8 | Not comparable0 vs 3 rows | Not comparable |
| Math | Not measured | 1.8 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
SWE-Rebench
Coding
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.
50K fresh input + 3K output tokens
GLM-4.7 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Composer 2 does not fit this workload in one request. GLM-4.7 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 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.
Composer 2
200K
GLM-4.7
200K
Composer 2
Not sourced
GLM-4.7
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Composer 2
Not published
GLM-4.7
No comparable hosted API rate
Composer 2
Not sourced
GLM-4.7
Not sourced
Composer 2
Not sourced
GLM-4.7
Not sourced
Composer 2
Not sourced
GLM-4.7
Not sourced
Composer 2
Reasoning
GLM-4.7
Reasoning
Composer 2
Proprietary
GLM-4.7
Open Weight
Composer 2
Proprietary
GLM-4.7
Open Weight
Composer 2
2026-03-19
GLM-4.7
2025-10-01
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
SWE Multilingual
Not directly comparable
SWE-Rebench
Shared sourceGLM-4.7 leads this result
React Native Evals
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
SWE-bench Verified
Not directly comparable
LiveCodeBench
Not directly comparable
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. 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.
Both models list the same context window, 200K.
Last updated August 7, 2026
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