Claude Haiku 4.5 vs GLM 4.7
Updated October 21, 2026. 61 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Claude Haiku 4.5 for coding: it scores 49.8 on the Vibe Coding Index against 33.6 for GLM 4.7.
Pick GLM 4.7 when output tokens cost 2.5x less, or when a solved task costs $0.29 against $0.56.
- Shared results
- 61
- Claude Haiku 4.5 only
- 33
- GLM 4.7 only
- 19
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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.
Coding
Vibe Coding Index, four public coding benchmarks
Claude Haiku 4.5
Claude Haiku 4.5 scores 49.8 against 33.6.
3 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 4.7
GLM 4.7 leads, 22.2 against 16.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Claude Haiku 4.5 has a AA Agentic Index result, 8.0.
Directional only
Reasoning
GPQA Diamond
GLM 4.7
GLM 4.7 leads, 80.0% against 72.2%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 82.7%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.7
GLM 4.7 leads, 82.7% against 78.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 1122.
Same board, both measured
Long documents
Listed context window
GLM 4.7
GLM 4.7 takes 205K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
Claude Haiku 4.5 $0.0035, GLM 4.7 $0.0015 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
Claude Haiku 4.5 $0.56, GLM 4.7 $0.29.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Haiku 4.5 49.8 from 3 benchmarks, GLM 4.7 33.6 from 1 benchmark.
- Widest category
- Agentic work, 12.0 points apart on average over 1 shared benchmark.
- Widest single gap
- LiveCodeBench (Vals AI): Claude Haiku 4.5 41.2% against GLM 4.7 82.2%, 41.1 points apart on a 0 to 100 scale.
- Output price
- Claude Haiku 4.5 $5.00 against GLM 4.7 $1.98 per million output tokens, 2.5x apart.
- Context window
- Claude Haiku 4.5 takes 200K 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.
| Category | Claude Haiku 4.5 | Bars | GLM 4.7 | Ties |
|---|---|---|---|---|
| Coding | 1 | 8 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 5 | ||
| Math | 2 | 3 | ||
| Knowledge | 2 | 10 | ||
| Web design | 0 | 8 | ||
| General | 2 | 16 | ||
| All shared boards | 8 | 53 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index18
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.6% | Not measured | Only Claude Haiku 4.5 measured |
| SWE-bench Multilingual | 64.7% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench coding | 48.1% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena WebDev | 1330 | 1435 | GLM 4.7 |
| AA Coding Index | 43.9 | 45.3 | GLM 4.7 |
| LiveCodeBench (Vals AI) | 41.2% | 82.2% | GLM 4.7 |
| SWE-bench Verified (Vals AI) | 66.6% | 69.4% | GLM 4.7 |
| Vibe Code Bench (Vals AI) | 11.4% | Not measured | Only Claude Haiku 4.5 measured |
| Code migration (Vals AI) | 7.5% | Not measured | Only Claude Haiku 4.5 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Claude Haiku 4.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 43.8% | Not measured | Only Claude Haiku 4.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 89.4% | Only GLM 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.8% | Only GLM 4.7 measured |
| Terminal-Bench | 35.5% | 33.4% | Claude Haiku 4.5 |
| WeirdML | 45.4% | Not measured | Only Claude Haiku 4.5 measured |
| ALE-Bench | Not measured | 399.5 | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | 1454 | GLM 4.7 |
| LMArena Text: software and IT | 1439 | 1453 | GLM 4.7 |
| LMArena WebDev: React | 1326 | 1420 | GLM 4.7 |
| LMArena WebDev: HTML | 1318 | 1447 | GLM 4.7 |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.9 | 22.2 | GLM 4.7 |
| Epoch Capabilities Index | 142.2 | 143.6 | GLM 4.7 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| AA Agentic Index | 8.0 | Not measured | Only Claude Haiku 4.5 measured |
| Design Arena: agents building full-stack apps | Not measured | 1050 | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | Not measured | 1105 | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | Not measured | 1118 | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | Not measured | 1058 | Only GLM 4.7 measured |
| LiveBench research | 42.2% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench browser | 40.0% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench long agentic | 11.1% | Not measured | Only Claude Haiku 4.5 measured |
| Finance Agent (Vals AI) | 31.0% | Not measured | Only Claude Haiku 4.5 measured |
| Tax Agent Bench (Vals AI) | 5.9% | Not measured | Only Claude Haiku 4.5 measured |
| Legal Research Bench (Vals AI) | 10.6% | Not measured | Only Claude Haiku 4.5 measured |
| Excel Modeling Benchmark (Vals AI) | 22.7% | Not measured | Only Claude Haiku 4.5 measured |
| τ²-Bench Telecom | Not measured | 95.9% | Only GLM 4.7 measured |
| BALROG | 31.2% | Not measured | Only Claude Haiku 4.5 measured |
| DeepResearch Bench | 45.5% | Not measured | Only Claude Haiku 4.5 measured |
| Vending-Bench 2 | $459 | $2,377 | GLM 4.7 |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 80.0% | GLM 4.7 |
| LiveBench reasoning | 61.7% | Not measured | Only Claude Haiku 4.5 measured |
| MMMU Pro (Vals AI) | 46.1% | Not measured | Only Claude Haiku 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.9% | Only GLM 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 27.4% | Only GLM 4.7 measured |
| GPQA Diamond (Epoch AI run) | 71.2% | 83.3% | GLM 4.7 |
| ARC-AGI | 47.7% | Not measured | Only Claude Haiku 4.5 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only Claude Haiku 4.5 measured |
| SimpleBench | Not measured | 47.7% | Only GLM 4.7 measured |
| Chess puzzles | 8.0% | 21.0% | GLM 4.7 |
| LMArena Text: hard prompts | 1421 | 1443 | GLM 4.7 |
| LMArena Text: expert | 1443 | 1424 | Claude Haiku 4.5 |
| LMArena Text: hard prompts in English | 1435 | 1452 | GLM 4.7 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| AIME | 82.7% | 93.3% | GLM 4.7 |
| MGSM (Vals AI) | 92.2% | 88.2% | Claude Haiku 4.5 |
| AA Math Index | Not measured | 95.0 | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 95.0% | Only GLM 4.7 measured |
| OTIS Mock AIME | 66.7% | 83.3% | GLM 4.7 |
| ProofBench | Not measured | 6.0% | Only GLM 4.7 measured |
| MATH level 5 | 96.4% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: math | 1398 | 1424 | GLM 4.7 |
| LMArena Text: mathematical work | 1430 | 1411 | Claude Haiku 4.5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| MMLU Pro | 78.7% | 82.7% | GLM 4.7 |
| LegalBench (Vals AI) | 81.2% | 83.4% | GLM 4.7 |
| CaseLaw (Vals AI) | 56.5% | 54.9% | Claude Haiku 4.5 |
| CorpFin (Vals AI) | 60.6% | 46.4% | Claude Haiku 4.5 |
| TaxEval (Vals AI) | 67.5% | 68.8% | GLM 4.7 |
| MortgageTax (Vals AI) | 62.2% | Not measured | Only Claude Haiku 4.5 measured |
| MedQA (Vals AI) | 79.6% | 93.7% | GLM 4.7 |
| MedCode (Vals AI) | 32.7% | 32.8% | GLM 4.7 |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.6% | Only GLM 4.7 measured |
| SimpleQA Verified | 13.2% | 32.2% | GLM 4.7 |
| LMArena Text: science | 1406 | 1457 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1394 | 1469 | GLM 4.7 |
| LMArena Text: legal and government | 1400 | 1444 | GLM 4.7 |
| LMArena Text: business and finance | 1400 | 1429 | GLM 4.7 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 1226 | GLM 4.7 |
| Design Arena: UI components | 1100 | 1199 | GLM 4.7 |
| Design Arena: game dev | 1097 | 1189 | GLM 4.7 |
| Design Arena: data viz | 1124 | 1195 | GLM 4.7 |
| Design Arena: 3D | 1072 | 1191 | GLM 4.7 |
| Design Arena: all code categories | 1115 | 1217 | GLM 4.7 |
| Design Arena: SVG | 1025 | 1142 | GLM 4.7 |
| Design Arena: ASCII art | 1139 | 1165 | GLM 4.7 |
| LiveBench design | 59.9% | Not measured | Only Claude Haiku 4.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index216
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| LiveBench data analysis | 59.3% | Not measured | Only Claude Haiku 4.5 measured |
| Public Benefits Bench (Vals AI) | 54.3% | Not measured | Only Claude Haiku 4.5 measured |
| SAGE (Vals AI) | 31.8% | Not measured | Only Claude Haiku 4.5 measured |
| MedScribe (Vals AI) | 85.2% | 68.6% | Claude Haiku 4.5 |
| Vals Multimodal Index | 42.9% | Not measured | Only Claude Haiku 4.5 measured |
| IFBench | Not measured | 67.9% | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | Not measured | 71.0% | Only GLM 4.7 measured |
| DTBench | 73.6% | Not measured | Only Claude Haiku 4.5 measured |
| LMCA | 30.9% | Not measured | Only Claude Haiku 4.5 measured |
| CL-bench | Not measured | 15.9% | Only GLM 4.7 measured |
| ExploitBench | 13.7% | Not measured | Only Claude Haiku 4.5 measured |
| CL-bench Life | Not measured | 10.9% | Only GLM 4.7 measured |
| LMArena Text | 1397 | 1435 | GLM 4.7 |
| LMArena Text: instruction following | 1414 | 1412 | Claude Haiku 4.5 |
| LMArena Text: creative writing | 1371 | 1401 | GLM 4.7 |
| LMArena Text: English | 1414 | 1453 | GLM 4.7 |
| LMArena Text: non-English | 1377 | 1417 | GLM 4.7 |
| LMArena Text: Chinese | 1418 | 1495 | GLM 4.7 |
| LMArena Text: German | 1374 | 1426 | GLM 4.7 |
| LMArena Text: Japanese | 1338 | 1439 | GLM 4.7 |
| LMArena Text: Korean | 1346 | 1399 | GLM 4.7 |
| LMArena Text: French | 1411 | 1432 | GLM 4.7 |
| LMArena Text: Spanish | 1421 | 1434 | GLM 4.7 |
| LMArena Text: Russian | 1381 | 1423 | GLM 4.7 |
| LMArena Text: Polish | 1343 | 1435 | GLM 4.7 |
| LMArena Text: multi-turn | 1409 | 1446 | GLM 4.7 |
| LMArena Text: longer queries | 1427 | 1432 | GLM 4.7 |
| LMArena Text: writing and language | 1386 | 1411 | GLM 4.7 |
| LMArena Text: entertainment and media | 1369 | 1400 | GLM 4.7 |
| LMArena Document | 1420 | Not measured | Only Claude Haiku 4.5 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Haiku 4.5 | GLM 4.7 | Leader |
|---|---|---|---|
| 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
GLM 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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.
| Specification | Anthropic Claude Haiku 4.5 | Z.ai GLM 4.7 |
|---|---|---|
| Maker | Anthropic | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | October 15, 2025 | December 22, 2025 |
| Context window | 200K | 205K |
| Input $/Mtok | $1.00 | $0.54 |
| Output $/Mtok | $5.00 | $1.98 |
| Coding index | 49.8 | 33.6 |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.56 | $0.29 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Haiku 4.5 better than GLM 4.7 for coding?
Pick Claude Haiku 4.5 for coding: it scores 49.8 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 2.5x less, or when a solved task costs $0.29 against $0.56.
Which is cheaper, Claude Haiku 4.5 or GLM 4.7?
Claude Haiku 4.5 costs $1.00 per million input tokens and $5.00 per million output tokens; GLM 4.7 costs $0.54 and $1.98. Per solved SWE-bench Verified task, Claude Haiku 4.5 costs $0.56 and GLM 4.7 $0.29.
Which has the bigger context window, Claude Haiku 4.5 or GLM 4.7?
GLM 4.7, with 205K tokens against 200K.
How many benchmarks is the Claude Haiku 4.5 vs GLM 4.7 comparison based on?
61 benchmarks have a result for both models as of October 21, 2026. Claude Haiku 4.5's coding index rests on 3 independent benchmarks and GLM 4.7's on 1. Where only one model has a result, the page says so instead of naming a winner.