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Claude Haiku 5.5 vs GLM 4.7

Claude Haiku 5.5 leads GLM 4.7 on coding, 70.7 to 55.1 on the Vibe Coding Index, and Claude Haiku 5.5 costs 4.4x less per output token ($0.50 vs $2.20 per million). Updated October 21, 2026; 21 benchmarks have a result for both.

Replace Claude Haiku 5.5 Replace GLM 4.7
Claude Haiku 5.5Anthropic
Coding
70.7
Overall
67.6
Output $/M
$0.50
20
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
1
vs boards won of 21 shared

Claude Haiku 5.5 or GLM 4.7: which to pick

Pick Claude Haiku 5.5 for coding: it scores 70.7 on the Vibe Coding Index against 55.1 for GLM 4.7.

Pick GLM 4.7 for work measured by SimpleQA Verified, where it leads.

Model AClaude Haiku 5.5Anthropic70.7/1005 independent benchmarksNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
21
Claude Haiku 5.5 only
42
GLM 4.7 only
60
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Claude Haiku 5.5 70.7 from 5 benchmarks, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Math, 49.3 points apart on average over 2 shared benchmarks.
Widest single gap
ProofBench: Claude Haiku 5.5 89.0% against GLM 4.7 6.0%, 83.0 points apart on a 0 to 100 scale.
Output price
Claude Haiku 5.5 $0.50 against GLM 4.7 $2.20 per million output tokens, 4.4x apart.
Context window
Claude Haiku 5.5 takes 1M tokens, GLM 4.7 205K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryClaude Haiku 5.5BarsGLM 4.7Ties
Coding30
Intelligence20
Agentic work10
Reasoning30
Math20
Knowledge11
Web design60
General20
All shared boards201
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
LiveBench coding91.4%Not measuredOnly Claude Haiku 5.5 measured
LMArena WebDev15861435Claude Haiku 5.5
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
Terminal-Bench 4 (Vals AI)36.4%Not measuredOnly Claude Haiku 5.5 measured
Vibe Code Bench (Vals AI)90.4%Not measuredOnly Claude Haiku 5.5 measured
Code migration (Vals AI)44.6%Not measuredOnly Claude Haiku 5.5 measured
ProgramBench (Vals AI)2.5%Not measuredOnly Claude Haiku 5.5 measured
IOI (Vals AI)47.3%Not measuredOnly Claude Haiku 5.5 measured
SWE-bench Multimodal (Vals AI)44.0%Not measuredOnly Claude Haiku 5.5 measured
Vibe Code Bench 1-100 (Vals AI)20.4%Not measuredOnly Claude Haiku 5.5 measured
CyberBench (Vals AI)75.5%Not measuredOnly Claude Haiku 5.5 measured
LiveCodeBench (Artificial Analysis run)Not measured89.4%Only GLM 4.7 measured
SciCode55.0%Not measuredOnly Claude Haiku 5.5 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured31.8%Only GLM 4.7 measured
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
FrontierCode46.4%Not measuredOnly Claude Haiku 5.5 measured
FrontierSWE43.8%Not measuredOnly Claude Haiku 5.5 measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: codingNot measured1454Only GLM 4.7 measured
LMArena Text: software and ITNot measured1453Only GLM 4.7 measured
LMArena WebDev: React16051420Claude Haiku 5.5
LMArena WebDev: HTML15691447Claude Haiku 5.5
SciCode9.2%Not measuredOnly Claude Haiku 5.5 measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
AA Intelligence Index43.422.2Claude Haiku 5.5
Epoch Capabilities Index159.3143.6Claude Haiku 5.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1112Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
LiveBench research81.9%Not measuredOnly Claude Haiku 5.5 measured
LiveBench browser80.0%Not measuredOnly Claude Haiku 5.5 measured
LiveBench long agentic57.6%Not measuredOnly Claude Haiku 5.5 measured
SREBench (Vals AI)5.7%Not measuredOnly Claude Haiku 5.5 measured
Finance Agent (Vals AI)54.1%Not measuredOnly Claude Haiku 5.5 measured
Tax Agent Bench (Vals AI)31.9%Not measuredOnly Claude Haiku 5.5 measured
Legal Research Bench (Vals AI)43.3%Not measuredOnly Claude Haiku 5.5 measured
Harvey's Legal Agent Benchmark (Vals AI)4.2%Not measuredOnly Claude Haiku 5.5 measured
Excel Modeling Benchmark (Vals AI)59.0%Not measuredOnly Claude Haiku 5.5 measured
BioMysteryBench (Vals AI)70.4%Not measuredOnly Claude Haiku 5.5 measured
Terminal-Bench-Science (Vals AI)10.0%Not measuredOnly Claude Haiku 5.5 measured
Vals Science Index38.5%Not measuredOnly Claude Haiku 5.5 measured
τ²-Bench TelecomNot measured95.9%Only GLM 4.7 measured
APEX-Agents56.1%Not measuredOnly Claude Haiku 5.5 measured
Vending-Bench 2$3,602$2,377Claude Haiku 5.5
BrowseComp-PlusNot measured64.7%Only GLM 4.7 measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
LiveBench reasoning82.4%Not measuredOnly Claude Haiku 5.5 measured
MysteryMechanism (Vals AI)35.1%Not measuredOnly Claude Haiku 5.5 measured
GPQA Diamond (Artificial Analysis run)Not measured85.9%Only GLM 4.7 measured
Humanity's Last Exam (Artificial Analysis run)44.4%27.4%Claude Haiku 5.5
GPQA Diamond (Epoch AI run)89.6%83.3%Claude Haiku 5.5
ARC-AGI89.0%Not measuredOnly Claude Haiku 5.5 measured
ARC-AGI-261.3%Not measuredOnly Claude Haiku 5.5 measured
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzles55.0%21.0%Claude Haiku 5.5
Mystery game puzzles30.0%Not measuredOnly Claude Haiku 5.5 measured
EBR-bench43.8%Not measuredOnly Claude Haiku 5.5 measured
LMArena Text: hard promptsNot measured1443Only GLM 4.7 measured
LMArena Text: expertNot measured1424Only GLM 4.7 measured
LMArena Text: hard prompts in EnglishNot measured1452Only GLM 4.7 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math IndexNot measured95.0Only GLM 4.7 measured
AIME 2025 (Artificial Analysis run)Not measured95.0%Only GLM 4.7 measured
OTIS Mock AIME98.9%83.3%Claude Haiku 5.5
FrontierMath, tiers 1 to 375.1%Not measuredOnly Claude Haiku 5.5 measured
FrontierMath, tier 446.3%Not measuredOnly Claude Haiku 5.5 measured
ProofBench89.0%6.0%Claude Haiku 5.5
LMArena Text: mathNot measured1424Only GLM 4.7 measured
LMArena Text: mathematical workNot measured1411Only GLM 4.7 measured
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index11
Knowledge benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)51.5%32.8%Claude Haiku 5.5
MMLU Pro (Artificial Analysis run)Not measured85.6%Only GLM 4.7 measured
SimpleQA Verified23.8%32.2%GLM 4.7
LMArena Text: scienceNot measured1457Only GLM 4.7 measured
LMArena Text: medicine and healthcareNot measured1469Only GLM 4.7 measured
LMArena Text: legal and governmentNot measured1444Only GLM 4.7 measured
LMArena Text: business and financeNot measured1429Only GLM 4.7 measured
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index60
Web design benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
Design Arena: websites12631226Claude Haiku 5.5
Design Arena: UI components12911199Claude Haiku 5.5
Design Arena: game dev13141189Claude Haiku 5.5
Design Arena: data viz12951195Claude Haiku 5.5
Design Arena: 3D13431191Claude Haiku 5.5
Design Arena: all code categories12781217Claude Haiku 5.5
Design Arena: SVGNot measured1143Only GLM 4.7 measured
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
LiveBench design83.1%Not measuredOnly Claude Haiku 5.5 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
Vals Index58.7%Not measuredOnly Claude Haiku 5.5 measured
LiveBench data analysis53.1%Not measuredOnly Claude Haiku 5.5 measured
Public Benefits Bench (Vals AI)63.2%Not measuredOnly Claude Haiku 5.5 measured
SAGE (Vals AI)45.8%Not measuredOnly Claude Haiku 5.5 measured
MedScribe (Vals AI)89.5%68.6%Claude Haiku 5.5
IFBenchNot measured67.9%Only GLM 4.7 measured
Long context reasoning (AA-LCR)82.7%71.0%Claude Haiku 5.5
DTBench95.0%Not measuredOnly Claude Haiku 5.5 measured
LMCA51.1%Not measuredOnly Claude Haiku 5.5 measured
CL-benchNot measured15.9%Only GLM 4.7 measured
Furniture assembly47.5%Not measuredOnly Claude Haiku 5.5 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena TextNot measured1435Only GLM 4.7 measured
LMArena Text: instruction followingNot measured1412Only GLM 4.7 measured
LMArena Text: creative writingNot measured1401Only GLM 4.7 measured
LMArena Text: EnglishNot measured1453Only GLM 4.7 measured
LMArena Text: non-EnglishNot measured1417Only GLM 4.7 measured
LMArena Text: ChineseNot measured1495Only GLM 4.7 measured
LMArena Text: GermanNot measured1426Only GLM 4.7 measured
LMArena Text: JapaneseNot measured1439Only GLM 4.7 measured
LMArena Text: KoreanNot measured1399Only GLM 4.7 measured
LMArena Text: FrenchNot measured1432Only GLM 4.7 measured
LMArena Text: SpanishNot measured1434Only GLM 4.7 measured
LMArena Text: RussianNot measured1423Only GLM 4.7 measured
LMArena Text: PolishNot measured1435Only GLM 4.7 measured
LMArena Text: multi-turnNot measured1446Only GLM 4.7 measured
LMArena Text: longer queriesNot measured1432Only GLM 4.7 measured
LMArena Text: writing and languageNot measured1411Only GLM 4.7 measured
LMArena Text: entertainment and mediaNot measured1400Only GLM 4.7 measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Haiku 5.5 and GLM 4.7
BenchmarkClaude Haiku 5.5GLM 4.7Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Claude Haiku 5.5$0.0003Fits in one request
GLM 4.7$0.0017Fits in one request

Claude Haiku 5.5 costs less

Repository review

50K input + 3K output tokens

Claude Haiku 5.5$0.0065Fits in one request
GLM 4.7$0.037Fits in one request

Claude Haiku 5.5 costs less

Agent loop

200K input + 10K output tokens

Claude Haiku 5.5$0.025Fits in one request
GLM 4.7$0.142Exceeds the context window

Claude Haiku 5.5 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Claude Haiku 5.5 and GLM 4.7
SpecificationAnthropic Claude Haiku 5.5Z.ai GLM 4.7
MakerAnthropicZ.ai
WeightsNo public weights listedOpen weights
ReleasedOctober 7, 2026December 22, 2025
Context window1M205K
Input $/Mtok$0.10$0.60
Output $/Mtok$0.50$2.20
Coding index70.755.1
Independent measurements behind it52
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Haiku 5.5 better than GLM 4.7 for coding?

Pick Claude Haiku 5.5 for coding: it scores 70.7 on the Vibe Coding Index against 55.1 for GLM 4.7. Pick GLM 4.7 for work measured by SimpleQA Verified, where it leads.

Which is cheaper, Claude Haiku 5.5 or GLM 4.7?

Claude Haiku 5.5 costs $0.10 per million input tokens and $0.50 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, Claude Haiku 5.5 or GLM 4.7?

Claude Haiku 5.5, with 1M tokens against 205K.

How many benchmarks is the Claude Haiku 5.5 vs GLM 4.7 comparison based on?

21 benchmarks have a result for both models as of October 21, 2026. Claude Haiku 5.5's coding index rests on 5 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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