Long documents
Prompts that approach the documented context limit
Claude Opus 4.6
Claude Opus 4.6 has the larger documented context window.
Confidence: documented
Claude Opus 4.6 vs GLM-5.1
Updated September 23, 2026. Rank says Claude Opus 4.6 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
Claude Opus 4.6 has the higher public score estimate, 64.33 versus 57.71, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 13
- Claude Opus 4.6 only
- 21
- GLM-5.1 only
- 13
- Like-for-like categories
- 1 / 8
Supported: Claude Opus 4.6 and GLM-5.1How 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.
Chat turn cost
1K fresh input + 500 output tokens
GLM-5.1
GLM-5.1 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
Repository review cost
50K fresh input + 3K output tokens
GLM-5.1
GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
No clear pick
The like-for-like coding result is a practical tie on the public lane (within 0.5 points).
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-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-5.1 does not fit this workload in one request. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
51.4Claude Opus 4.651.5GLM-5.1
Like-for-like · BenchAlign v5.6
The like-for-like coding row is a practical tie.
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.
21 Claude Opus 4.6 only
13 Shared
13 GLM-5.1 only
3 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.
BrowseCompAgentic
Normalized gap 15.7Claude Opus 4.6:83.7%GLM-5.1:68%FrontierMath v2 (Tier 4)Math
Normalized gap 10.4Claude Opus 4.6:22.900%GLM-5.1:12.500%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 7.3Claude Opus 4.6:40.700%GLM-5.1:33.448%SWE-bench ProCoding
Normalized gap 5.0Claude Opus 4.6:53.4%GLM-5.1:58.4%SWE-RebenchCoding
Normalized gap 2.6Claude Opus 4.6:65.3%GLM-5.1:62.7%
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 v5.6 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 4.6
- 51.4
- Supported · #37/135
- GLM-5.1
- 51.5
- Supported · #36/135
- Basis
- BenchAlign v5.6 lane · 8 vs 7 public rows
- Reading
- Practical tie
Agentic
Directional only- Claude Opus 4.6
- 44.2
- Supported · #40/105
- GLM-5.1
- 42.1
- Estimated · #43/105
- Basis
- BenchAlign v5.6 lane · 10 vs 9 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Opus 4.6
- 57.3
- Estimated · #36/160
- GLM-5.1
- 50.2
- Supported · #54/160
- Basis
- BenchAlign v5.6 lane · 9 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- Claude Opus 4.6
- 51.0
- #79/124
- GLM-5.1
- 92.4
- #4/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.6
- 67.1
- Unranked · 2 rankable rows
- GLM-5.1
- 71.7
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.6
- 59.6
- #30/50
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.6
- Not ranked
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.6
- 58.5
- Unranked · 3 rankable rows
- GLM-5.1
- 63.8
- #3/7
- Basis
- Provisional lane · 2 vs 4 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.6 | GLM-5.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 51.4Supported · #37/135 | 51.5Supported · #36/135 | Like-for-likeBenchAlign v5.6 lane · 8 vs 7 public rows | Practical tie |
| Agentic | 44.2Supported · #40/105 | 42.1Estimated · #43/105 | Directional onlyBenchAlign v5.6 lane · 10 vs 9 public rows | Directional only |
| Knowledge | 57.3Estimated · #36/160 | 50.2Supported · #54/160 | Directional onlyBenchAlign v5.6 lane · 9 vs 4 public rows | Directional only |
| Instruction following | 51.0#79/124 | 92.4#4/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 67.1Unranked · 2 rankable rows | 71.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 59.6#30/50 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 58.5Unranked · 3 rankable rows | 63.8#3/7 | Not comparableProvisional lane · 2 vs 4 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) 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
- Claude Opus 4.6
- $0.0175
- Fits in one request
- GLM-5.1
- $0.0036
- Fits in one request
GLM-5.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.6
- $0.325
- Fits in one request
- GLM-5.1
- $0.0832
- Fits in one request
GLM-5.1 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 4.6
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- GLM-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
GLM-5.1 does not fit this workload in one request. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input 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.
SpecificationClaude Opus 4.6GLM-5.1
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6
1M
GLM-5.1
203K
API model ID
Claude Opus 4.6
Not sourced
GLM-5.1
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 4.6
Not published
GLM-5.1
Not published
Documented inputs
Claude Opus 4.6
Not sourced
GLM-5.1
Not sourced
Documented outputs
Claude Opus 4.6
Not sourced
GLM-5.1
Not sourced
Provider availability
Claude Opus 4.6
Not sourced
GLM-5.1
Not sourced
Reasoning profile
Claude Opus 4.6
Non-Reasoning
GLM-5.1
Reasoning
Weight access
Claude Opus 4.6
Proprietary
GLM-5.1
Open Weight
License
Claude Opus 4.6
Proprietary
GLM-5.1
Open Weight
Release date
Claude Opus 4.6
2026-02-01
GLM-5.1
2026-04-07
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 4.6 has the higher public score estimate, 64.33 versus 57.71, 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
- Claude Opus 4.6 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 4.6 or GLM-5.1?
Claude Opus 4.6 has the higher public score estimate, 64.33 versus 57.71, 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 4.6 or GLM-5.1?
The like-for-like coding row is a practical tie on the public lane, 51.4 against 51.5, inside the 0.5-point band BenchLM treats as level.
Which is better for agentic tasks, Claude Opus 4.6 or GLM-5.1?
Claude Opus 4.6 scores higher for agentic tasks on the public lane, 44.2 to 42.1. GLM-5.1 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, Claude Opus 4.6 or GLM-5.1?
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.0036 on GLM-5.1; repository review costs $0.325 and $0.0832; the cache-heavy agent loop costs $1.35 and $0.352. GLM-5.1 does not fit this workload in one request. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.6 or GLM-5.1?
Claude Opus 4.6 has the larger documented context window: 1M, compared with 203K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Claude Opus 4.6
API / mo$22,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence47 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.665.4%SourceGLM-5.163.5%SourceClaude Opus 4.6 leads this result
BrowseComp
Claude Opus 4.683.7%SourceGLM-5.168%SourceClaude Opus 4.6 leads this result
OSWorld-Verified
Claude Opus 4.672.7%SourceGLM-5.1—Not directly comparable
Claw-Eval
Shared sourceClaude Opus 4.670.4%GLM-5.162.3%Claude Opus 4.6 leads this result
DeepSearchQA
Claude Opus 4.673.7%SourceGLM-5.1—Not directly comparable
CyberGym
Shared sourceClaude Opus 4.666.6%GLM-5.168.7%GLM-5.1 leads this result
Gert Labs
Shared sourceClaude Opus 4.661.85%GLM-5.160.11%Claude Opus 4.6 leads this result
ResearchClawBench
Shared sourceClaude Opus 4.619.9%GLM-5.118.2%Claude Opus 4.6 leads this result
JobBench
Claude Opus 4.636.7%SourceGLM-5.1—Not directly comparable
ApprenticeBench
Claude Opus 4.65%SourceGLM-5.1—Not directly comparable
τ³-bench results
Claude Opus 4.6—GLM-5.170.6%SourceNot directly comparable
MCP Atlas
Claude Opus 4.6—GLM-5.171.8%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6—GLM-5.156.9%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.680.8%SourceGLM-5.1—Not directly comparable
SWE-bench Verified*
Claude Opus 4.675.6%SourceGLM-5.1—Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%SourceGLM-5.1—Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%SourceGLM-5.158.4%SourceGLM-5.1 leads this result
SWE-Rebench
Claude Opus 4.665.3%SourceGLM-5.162.7%SourceClaude Opus 4.6 leads this result
React Native Evals
Claude Opus 4.684.1%SourceGLM-5.1—Not directly comparable
Vibe Code Bench
Shared sourceClaude Opus 4.657.57%GLM-5.131.46%Claude Opus 4.6 leads this result
FrontierCode 1.1 Main
Claude Opus 4.626.9%SourceGLM-5.1—Not directly comparable
NL2Repo
Claude Opus 4.6—GLM-5.142.7%SourceNot directly comparable
OpenHarmony Bench
Claude Opus 4.6—GLM-5.152.3%SourceNot directly comparable
LiveCodeBench (Vals)
Claude Opus 4.6—GLM-5.181.4%SourceNot directly comparable
SWE-bench (Vals)
Claude Opus 4.6—GLM-5.176.4%SourceNot directly comparable
Multimodal
Knowledge
GPQA
Claude Opus 4.691.3%SourceGLM-5.1—Not directly comparable
GPQA-D
Claude Opus 4.689.2%SourceGLM-5.186.2%SourceClaude Opus 4.6 leads this result
SuperGPQA
Claude Opus 4.695%SourceGLM-5.1—Not directly comparable
MMLU-Pro
Claude Opus 4.682%SourceGLM-5.1—Not directly comparable
MMLU-Pro (Arcee)
Claude Opus 4.689.1%SourceGLM-5.1—Not directly comparable
HLE
Claude Opus 4.653%SourceGLM-5.152.3%SourceClaude Opus 4.6 leads this result
HLE w/o tools
Claude Opus 4.640%SourceGLM-5.1—Not directly comparable
HealthBench Hard
Claude Opus 4.614.8%SourceGLM-5.1—Not directly comparable
MedXpertQA (Text)
Claude Opus 4.652.1%SourceGLM-5.1—Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6—GLM-5.184.5%SourceNot directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6—GLM-5.186.9%SourceNot directly comparable
Math
AIME25 (Arcee)
Claude Opus 4.699.8%SourceGLM-5.1—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.640.700%GLM-5.133.448%Claude Opus 4.6 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Opus 4.622.900%GLM-5.112.500%Claude Opus 4.6 leads this result
AIME26
Claude Opus 4.6—GLM-5.195.3%SourceNot directly comparable
HMMT Nov 2025
Claude Opus 4.6—GLM-5.194.0%SourceNot directly comparable
HMMT Feb 2026
Claude Opus 4.6—GLM-5.182.6%SourceNot directly comparable
MMAnswerBench
Claude Opus 4.6—GLM-5.183.8%SourceNot directly comparable
47 public results · 13 shared
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Last updated September 23, 2026