Model comparison
Claude Haiku 4.5 vs GLM-5
GLM-5 is the stronger model overall, scoring 46.1 to 39.5 on the Noometry Index.
Last verified . 35 shared benchmarks.
Summary
- They share 35 benchmarks with published results for both. Claude Haiku 4.5 scores higher in 1 category and GLM-5 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.3 to 37.7.
- The biggest single-benchmark swing is NYT Connections (extended): 14.3% for Claude Haiku 4.5 and 74.8% for GLM-5.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $1 / $5 for Claude Haiku 4.5.
- GLM-5 accepts more context: 205K tokens versus 200K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| Claude Haiku 4.5 | GLM-5 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 39.5 | 46.1 |
| Released | 2025-10-15 | 2026-02-11 |
| Weights | Proprietary | Open |
| Context window | 200K | 205K |
| Max output | 64K | 131K |
| Input $ / M tokens | $1 | $1 |
| Output $ / M tokens | $5 | $3.20 |
| Results tracked | 53 | 45 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5 leads
Claude Haiku 4.5: 44.0 (#78), GLM-5: 49.0 (#52)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| SWE-bench Verified (bash only) | 66.6% | 72.8% |
| LMArena WebDev | 1330 | 1434 |
| SWE-bench Multilingual | 64.7% | 69.7% |
| WeirdML | 45.4% | 48.2% |
| LMArena Coding | 1453 | 1461 |
| ALE-Bench | 653.48 | 765.62 |
| SWE-bench Verified | — | 72.1% |
| SciCode | 43.3% | — |
Agentic & Tool Use Claude Haiku 4.5 leads
Claude Haiku 4.5: 33.6 (#52), GLM-5: 31.1 (#71)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| Terminal-Bench | 35.5% | 52.4% |
| Vending-Bench 2 | 458.89 | 4,432 |
| Berkeley Function Calling Leaderboard | 68.7% | — |
| τ²-bench Airline | — | 82.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 73.7% |
| τ²-bench Telecom | — | 86.8% |
| DeepResearch Bench | 45.5% | — |
| BALROG | 31.2% | — |
| ExploitBench | 13.7% | — |
Reasoning GLM-5 leads
Claude Haiku 4.5: 15.1 (#320), GLM-5: 27.6 (#116)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| ARC-AGI-2 | 4% | 4.9% |
| NYT Connections (extended) | 14.3% | 74.8% |
| ARC-AGI-1 | 47.7% | 44.7% |
| Chess Puzzles | 8% | 10% |
| LMArena Hard Prompts | 1420 | 1452 |
| Epoch Capabilities Index | 142.41 | 145.83 |
| ForecastBench | 61.4 | 61 |
| SimpleBench | — | 53.2% |
| Kagi LLM Benchmark | — | 75% |
| CritPt | 0% | — |
| DTBench | 73.6% | — |
| LMCA | 30.9% | — |
Math GLM-5 leads
Claude Haiku 4.5: 44.9 (#78), GLM-5: 46.4 (#71)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.7% | 80% |
| LMArena Math | 1396 | 1440 |
| FrontierMath (Feb 2025 set) | 5.9% | 16.4% |
| FrontierMath Tier 4 (v1) | 2.1% | 2.1% |
| MathArena Final-Answer Competitions | — | 65.7% |
| Omni-MATH | 56.1% | — |
| MATH Level 5 | 96.4% | — |
Knowledge GLM-5 leads
Claude Haiku 4.5: 37.7 (#153), GLM-5: 52.3 (#64)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| GPQA Diamond | 71.2% | 87.8% |
| Vectara Hallucination Rate | 9.8% | 10.1% |
| LMArena Expert | 1442 | 1454 |
| SimpleQA Verified | 13.2% | — |
| MMLU-Pro | 77.7% | — |
| GPQA (HELM) | 60.5% | — |
Multimodal Not comparable
Claude Haiku 4.5: 26.8 (#118), GLM-5: —
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |
Multilingual GLM-5 leads
Claude Haiku 4.5: 49.9 (#129), GLM-5: 53.7 (#58)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| LMArena Non-English | 1377 | 1430 |
| LMArena Chinese | 1417 | 1511 |
| LMArena French | 1408 | 1455 |
| LMArena German | 1375 | 1445 |
| LMArena Japanese | 1339 | 1416 |
| LMArena Korean | 1347 | 1423 |
| LMArena Russian | 1381 | 1436 |
| LMArena Spanish | 1420 | 1454 |
Instruction Following GLM-5 leads
Claude Haiku 4.5: 71.4 (#149), GLM-5: 75.2 (#67)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| LMArena Instruction Following | 1414 | 1428 |
| IFEval | 80.1% | — |
Long Context GLM-5 leads
Claude Haiku 4.5: 43.6 (#92), GLM-5: 44.7 (#60)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| LMArena Longer Query | 1427 | 1446 |
| CL-bench | — | 18.7% |
Writing & Preference GLM-5 leads
Claude Haiku 4.5: 57.9 (#123), GLM-5: 66.0 (#38)
| Benchmark | Claude Haiku 4.5 | GLM-5 |
|---|---|---|
| LMArena Text | 1396 | 1446 |
| LMArena Creative Writing | 1372 | 1439 |
| LMArena Multi-Turn | 1409 | 1456 |
| EQ-Bench Creative Writing | — | 1601 |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
Frequently asked questions
Is Claude Haiku 4.5 better than GLM-5?
GLM-5 is the stronger model overall, scoring 46.1 to 39.5 on the Noometry Index.
Which is cheaper, Claude Haiku 4.5 or GLM-5?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; Claude Haiku 4.5 lists at $1 and $5.
Is Claude Haiku 4.5 or GLM-5 better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5 does, with 205K tokens against 200K.
How many benchmarks do Claude Haiku 4.5 and GLM-5 share?
35 benchmarks have published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and GLM-5 has 45.