Agentic
Like-for-like- Claude Opus 4.7
- 55.8
- Supported · #37/153
- GLM-5.2
- 58.5
- Supported · #29/153
- Basis
- BenchAlign lane · 5 vs 6 public rows
- Reading
- GLM-5.2 leads · intervals overlap
Claude Opus 4.7 vs GLM-5.2
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Opus 4.7 has the higher public score estimate, 70.36 versus 68.19, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 4.7
Claude Opus 4.7 leads on the public coding lane, 62.8 to 61, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the public agentic lane, 58.5 to 55.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Claude Opus 4.7 only
6 Shared
19 GLM-5.2 only
- Shared results
- 6
- Claude Opus 4.7 only
- 8
- GLM-5.2 only
- 19
- Like-for-like categories
- 2 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Like-for-like- Claude Opus 4.7
- 62.8
- Supported · #14/152
- GLM-5.2
- 61.0
- Supported · #19/152
- Basis
- BenchAlign lane · 5 vs 8 public rows
- Reading
- Claude Opus 4.7 leads · intervals overlap
Knowledge
Directional only- Claude Opus 4.7
- 64.6
- Estimated · #26/183
- GLM-5.2
- 60.7
- Supported · #35/183
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.7
- Not ranked
- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.7
- 60.8
- Unranked · 2 rankable rows
- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7
- Not ranked
- GLM-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7
- Not ranked
- GLM-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7
- Not ranked
- GLM-5.2
- 89.8
- #22/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 | GLM-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.8Supported · #37/153 | 58.5Supported · #29/153 | Like-for-likeBenchAlign lane · 5 vs 6 public rows | GLM-5.2 leads · intervals overlap |
| Coding | 62.8Supported · #14/152 | 61.0Supported · #19/152 | Like-for-likeBenchAlign lane · 5 vs 8 public rows | Claude Opus 4.7 leads · intervals overlap |
| Knowledge | 64.6Estimated · #26/183 | 60.7Supported · #35/183 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Reasoning | Not ranked | 74.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 60.8Unranked · 2 rankable rows | 80.7Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 89.8#22/123 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
LiveCodeBench (Vals)
Coding
MMLU-Pro (Vals)
Knowledge
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
- Claude Opus 4.7
- $0.0175
- Fits in one request
- GLM-5.2
- $0.0036
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7
- $0.325
- Fits in one request
- GLM-5.2
- $0.0832
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.7
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- GLM-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
GLM-5.2 has the lower modeled cost
Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.7GLM-5.2
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7
GLM-5.2
1M
API model ID
Claude Opus 4.7
claude-opus-4-7
Anthropic model ID documentationGLM-5.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.7
Not published
GLM-5.2
Not published
Documented inputs
Claude Opus 4.7
text, image
Anthropic model overviewGLM-5.2
Not sourced
Documented outputs
Claude Opus 4.7
GLM-5.2
Not sourced
Provider availability
Claude Opus 4.7
Generally Available · Claude API
Anthropic model overviewGLM-5.2
Not sourced
Reasoning profile
Claude Opus 4.7
Non-Reasoning
GLM-5.2
Reasoning
Weight access
Claude Opus 4.7
Proprietary
GLM-5.2
Open Weight
License
Claude Opus 4.7
Proprietary
GLM-5.2
Open Weight
Release date
Claude Opus 4.7
2026-04-16
GLM-5.2
2026-06-16
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
- Claude Opus 4.7 has the higher public score estimate, 70.36 versus 68.19, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.0832. Cache-heavy agent loop: $1.35 vs $0.352.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence33 rows
Agentic
Gert Labs
Claude Opus 4.765.59%SourceGLM-5.2—Not directly comparable
ResearchClawBench
Shared sourceClaude Opus 4.720.7%GLM-5.220.7%Tie
OSWorld 2.0
Claude Opus 4.713.9%SourceGLM-5.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.768.5%SourceGLM-5.267.8%SourceClaude Opus 4.7 leads this result
ApprenticeBench
Claude Opus 4.77%SourceGLM-5.2—Not directly comparable
Terminal-Bench 3.0
Claude Opus 4.7—GLM-5.24.6%SourceNot directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—GLM-5.281%SourceNot directly comparable
MCP Atlas
Claude Opus 4.7—GLM-5.276.8%SourceNot directly comparable
Toolathlon
Claude Opus 4.7—GLM-5.248.2%SourceNot directly comparable
Coding
Vibe Code Bench
Claude Opus 4.771.00%SourceGLM-5.2—Not directly comparable
React Native Evals
Claude Opus 4.782.8%SourceGLM-5.2—Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.738.5%SourceGLM-5.2—Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.785.1%SourceGLM-5.269.5%SourceClaude Opus 4.7 leads this result
SWE-bench (Vals)
Claude Opus 4.782.0%SourceGLM-5.282.8%SourceGLM-5.2 leads this result
SWE-bench Pro
Claude Opus 4.7—GLM-5.262.1%SourceNot directly comparable
NL2Repo
Claude Opus 4.7—GLM-5.248.9%SourceNot directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—GLM-5.281.0%SourceNot directly comparable
ProgramBench
Claude Opus 4.7—GLM-5.263.7%SourceNot directly comparable
cursorBench32
Claude Opus 4.7—GLM-5.255.0%SourceNot directly comparable
OpenHarmony Bench
Claude Opus 4.7—GLM-5.258.4%SourceNot directly comparable
Reasoning
CritPt
Claude Opus 4.7—GLM-5.220.9%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Claude Opus 4.790.2%SourceGLM-5.285.6%SourceClaude Opus 4.7 leads this result
MMLU-Pro (Vals)
Claude Opus 4.789.9%SourceGLM-5.286.7%SourceClaude Opus 4.7 leads this result
GPQA
Claude Opus 4.7—GLM-5.291.2%SourceNot directly comparable
GPQA-D
Claude Opus 4.7—GLM-5.291.2%SourceNot directly comparable
HLE
Claude Opus 4.7—GLM-5.254.7%SourceNot directly comparable
HLE w/o tools
Claude Opus 4.7—GLM-5.240.5%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.743.793%SourceGLM-5.2—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.722.917%SourceGLM-5.2—Not directly comparable
AIME26
Claude Opus 4.7—GLM-5.299.2%SourceNot directly comparable
HMMT Nov 2025
Claude Opus 4.7—GLM-5.294.4%SourceNot directly comparable
HMMT Feb 2026
Claude Opus 4.7—GLM-5.292.5%SourceNot directly comparable
MMAnswerBench
Claude Opus 4.7—GLM-5.291.0%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Opus 4.7 or GLM-5.2?
Claude Opus 4.7 has the higher public score estimate, 70.36 versus 68.19, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Opus 4.7 or GLM-5.2?
Claude Opus 4.7 leads the public coding lane, 62.8 to 61, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Opus 4.7 or GLM-5.2?
GLM-5.2 leads the public agentic tasks lane, 58.5 to 55.8, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Opus 4.7 or GLM-5.2?
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.0036 on GLM-5.2; repository review costs $0.325 and $0.0832; the cache-heavy agent loop costs $1.35 and $0.352. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.7 or GLM-5.2?
Both models list the same context window, 1M.
Related comparisons
Last updated September 14, 2026
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