Coding work
Code generation, repair, and software-engineering tasks
GLM-5.1
GLM-5.1 leads on the public coding lane, 51.5 to 19.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Claude Haiku 4.5 vs GLM-5.1
Updated September 23, 2026. Rank says GLM-5.1 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
Share or export
Decision reading
GLM-5.1 has the higher public score estimate, 57.71 versus 41.55, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 7
- Claude Haiku 4.5 only
- 3
- GLM-5.1 only
- 19
- Like-for-like categories
- 1 / 8
Estimated: Claude Haiku 4.5 · Supported: 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.
Long documents
Prompts that approach the documented context limit
GLM-5.1
GLM-5.1 has the larger documented context window.
Confidence: documentedChat 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-ratesAgentic 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. 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
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.
19.8Claude Haiku 4.551.5GLM-5.1
Like-for-like · BenchAlign v5.6
GLM-5.1 leads the like-for-like coding row.
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.
3 Claude Haiku 4.5 only
7 Shared
19 GLM-5.1 only
2 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.
LiveCodeBench (Vals)Coding
Normalized gap 40.2Claude Haiku 4.5:41.2%GLM-5.1:81.4%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 27.5Claude Haiku 4.5:5.903%GLM-5.1:33.448%FrontierMath v2 (Tier 4)Math
Normalized gap 10.4Claude Haiku 4.5:2.083%GLM-5.1:12.500%MMLU-Pro (Vals)Knowledge
Normalized gap 8.2Claude Haiku 4.5:78.7%GLM-5.1:86.9%
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 Haiku 4.5
- 19.8
- Supported · #131/135
- GLM-5.1
- 51.5
- Supported · #36/135
- Basis
- BenchAlign v5.6 lane · 4 vs 7 public rows
- Reading
- GLM-5.1 leads
Agentic
Directional only- Claude Haiku 4.5
- 20.5
- Supported · #85/105
- GLM-5.1
- 42.1
- Estimated · #43/105
- Basis
- BenchAlign v5.6 lane · 2 vs 9 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Haiku 4.5
- 30.0
- Estimated · #126/160
- GLM-5.1
- 50.2
- Supported · #54/160
- Basis
- BenchAlign v5.6 lane · 2 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Haiku 4.5
- Not ranked
- GLM-5.1
- 71.7
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Haiku 4.5
- Not ranked
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Haiku 4.5
- Not ranked
- GLM-5.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Haiku 4.5
- Not ranked
- GLM-5.1
- 92.4
- #4/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Haiku 4.5
- 28.8
- Unranked · 2 rankable rows
- GLM-5.1
- 63.8
- #3/7
- Basis
- Provisional lane · 2 vs 4 weighted rows
- Reading
- Not comparable
| Category | Claude Haiku 4.5 | GLM-5.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 19.8Supported · #131/135 | 51.5Supported · #36/135 | Like-for-likeBenchAlign v5.6 lane · 4 vs 7 public rows | GLM-5.1 leads |
| Agentic | 20.5Supported · #85/105 | 42.1Estimated · #43/105 | Directional onlyBenchAlign v5.6 lane · 2 vs 9 public rows | Directional only |
| Knowledge | 30.0Estimated · #126/160 | 50.2Supported · #54/160 | Directional onlyBenchAlign v5.6 lane · 2 vs 4 public rows | Directional only |
| Reasoning | Not ranked | 71.7Unranked · 2 rankable rows | 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 |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 92.4#4/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 28.8Unranked · 2 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 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.
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 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, 57.71 versus 41.55, 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.
Questions
Which is better, Claude Haiku 4.5 or GLM-5.1?
GLM-5.1 has the higher public score estimate, 57.71 versus 41.55, 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?
GLM-5.1 leads the public coding lane, 51.5 to 19.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Claude Haiku 4.5 or GLM-5.1?
GLM-5.1 scores higher for agentic tasks on the public lane, 42.1 to 20.5. 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 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.
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 evidence29 rows
Agentic
JobBench
Claude Haiku 4.516.0%SourceGLM-5.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Haiku 4.543.8%SourceGLM-5.156.9%SourceGLM-5.1 leads this result
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 4.576.2%SourceGLM-5.1—Not directly comparable
LiveCodeBench (Vals)
Claude Haiku 4.541.2%SourceGLM-5.181.4%SourceGLM-5.1 leads this result
SWE-bench (Vals)
Claude Haiku 4.566.6%SourceGLM-5.176.4%SourceGLM-5.1 leads this result
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
OpenHarmony Bench
Claude Haiku 4.5—GLM-5.152.3%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Claude Haiku 4.572.2%SourceGLM-5.184.5%SourceGLM-5.1 leads this result
MMLU-Pro (Vals)
Claude Haiku 4.578.7%SourceGLM-5.186.9%SourceGLM-5.1 leads this result
GPQA-D
Claude Haiku 4.5—GLM-5.186.2%SourceNot directly comparable
HLE
Claude Haiku 4.5—GLM-5.152.3%SourceNot directly comparable
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
29 public results · 7 shared
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Last updated September 23, 2026