Math
Directional only- Claude Haiku 4.5
- 4.9
- GLM-5.1
- 62.0
- Weighted basis
- 2 vs 4 rows
- Reading
- Directional only
Model comparison
Claude Haiku 4.5 vs GLM-5.1
Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GLM-5.1 has the higher public score estimate, 66.9 versus 55.77, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Long documents
Prompts that approach the documented context limit
GLM-5.1
GLM-5.1 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Claude Haiku 4.5
Claude Haiku 4.5 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
Claude Haiku 4.5
Claude Haiku 4.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Cache-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. Claude Haiku 4.5 does not fit this workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Claude Haiku 4.5 only
2 Shared
18 GLM-5.1 only
- Shared results
- 2
- Claude Haiku 4.5 only
- 3
- GLM-5.1 only
- 18
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Agentic
Not comparable- Claude Haiku 4.5
- Not measured
- GLM-5.1
- 65.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Coding
Not comparable- Claude Haiku 4.5
- 73.3
- GLM-5.1
- 61.3
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Haiku 4.5
- Not measured
- GLM-5.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Haiku 4.5
- Not measured
- GLM-5.1
- 52.3
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Haiku 4.5
- Not measured
- GLM-5.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Haiku 4.5
- Not measured
- GLM-5.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Haiku 4.5
- Not measured
- GLM-5.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Haiku 4.5 | GLM-5.1 | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 4.9 | 62.0 | Directional only2 vs 4 rows | Directional only |
| Agentic | Not measured | 65.4 | Not comparable0 vs 2 rows | Not comparable |
| Coding | 73.3 | 61.3 | Not comparable1 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 52.3 | Not comparable0 vs 1 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 |
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.
FrontierMath v2 (Tiers 1-3)
Math
FrontierMath v2 (Tier 4)
Math
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 Haiku 4.5
- $0.0035
- Fits in one request
- GLM-5.1
- $0.0036
- Fits in one request
Claude Haiku 4.5 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Haiku 4.5
- $0.065
- Fits in one request
- GLM-5.1
- $0.0832
- Fits in one request
Claude Haiku 4.5 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 Haiku 4.5
- $0.09
- Does not fit in one request
- GLM-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
Claude Haiku 4.5 does not fit this workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 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 Haiku 4.5GLM-5.1
Context window
Maximum documented context; output-token limits may be lower.
Claude Haiku 4.5
GLM-5.1
203K
API model ID
Claude Haiku 4.5
claude-haiku-4-5-20251001
Claude API pricingGLM-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 Haiku 4.5
$0.1 per 1M cached input tokens
Claude API pricingGLM-5.1
Not published
Documented inputs
Claude Haiku 4.5
Not sourced
GLM-5.1
Not sourced
Documented outputs
Claude Haiku 4.5
Not sourced
GLM-5.1
Not sourced
Provider availability
Claude Haiku 4.5
Not sourced
GLM-5.1
Not sourced
Reasoning profile
Claude Haiku 4.5
Non-Reasoning
GLM-5.1
Reasoning
Weight access
Claude Haiku 4.5
Proprietary
GLM-5.1
Open Weight
License
Claude Haiku 4.5
Proprietary
GLM-5.1
Open Weight
Release date
Claude Haiku 4.5
2025-10-15
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
- GLM-5.1 has the higher public score estimate, 66.9 versus 55.77, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.065 vs $0.0832. Cache-heavy agent loop: $0.09 vs $0.352.
- Context tradeoff
- GLM-5.1 has the larger documented window (203K).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Claude Haiku 4.5
API / mo$4,500
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 evidence23 rows
Agentic
JobBench
Claude Haiku 4.516.0%SourceGLM-5.1—Not directly comparable
Terminal-Bench 2.0
Claude Haiku 4.5—GLM-5.163.5%SourceNot directly comparable
BrowseComp
Claude Haiku 4.5—GLM-5.168%SourceNot directly comparable
τ³-bench results
Claude Haiku 4.5—GLM-5.170.6%SourceNot directly comparable
MCP Atlas
Claude Haiku 4.5—GLM-5.171.8%SourceNot directly comparable
CyberGym
Claude Haiku 4.5—GLM-5.168.7%SourceNot directly comparable
Claw-Eval
Claude Haiku 4.5—GLM-5.162.3%SourceNot directly comparable
Gert Labs
Claude Haiku 4.5—GLM-5.160.11%SourceNot directly comparable
ResearchClawBench
Claude Haiku 4.5—GLM-5.118.2%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Haiku 4.573.3%SourceGLM-5.1—Not directly comparable
VulcanBench v3
Claude Haiku 4.578.3%SourceGLM-5.1—Not directly comparable
SWE-bench Pro
Claude Haiku 4.5—GLM-5.158.4%SourceNot directly comparable
NL2Repo
Claude Haiku 4.5—GLM-5.142.7%SourceNot directly comparable
SWE-Rebench
Claude Haiku 4.5—GLM-5.162.7%SourceNot directly comparable
Vibe Code Bench
Claude Haiku 4.5—GLM-5.131.46%SourceNot directly comparable
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Haiku 4.55.903%GLM-5.133.448%GLM-5.1 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Haiku 4.52.083%GLM-5.112.500%GLM-5.1 leads this result
AIME26
Claude Haiku 4.5—GLM-5.195.3%SourceNot directly comparable
HMMT Nov 2025
Claude Haiku 4.5—GLM-5.194.0%SourceNot directly comparable
HMMT Feb 2026
Claude Haiku 4.5—GLM-5.182.6%SourceNot directly comparable
MMAnswerBench
Claude Haiku 4.5—GLM-5.183.8%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Haiku 4.5 or GLM-5.1?
GLM-5.1 has the higher public score estimate, 66.9 versus 55.77, 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 Haiku 4.5 or GLM-5.1?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Claude Haiku 4.5 or GLM-5.1?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Claude Haiku 4.5 or GLM-5.1?
For the stated presets, chat costs $0.0035 on Claude Haiku 4.5 and $0.0036 on GLM-5.1; repository review costs $0.065 and $0.0832; the cache-heavy agent loop costs $0.09 and $0.352. Claude Haiku 4.5 does not fit this workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Haiku 4.5 or GLM-5.1?
GLM-5.1 has the larger documented context window: 203K, compared with 200K.
Related comparisons
Last updated August 7, 2026
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