Model comparison
Claude Fable 5 vs GLM-4.6
Claude Fable 5 is the stronger model overall, scoring 66.8 to 41.4 on the Noometry Index. GLM-4.6 costs 20× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. Claude Fable 5 scores higher in 9 categories and GLM-4.6 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Fable 5 leads 76.8 to 23.7.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 91.4% for Claude Fable 5 and 47.4% for GLM-4.6.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $10 / $50 for Claude Fable 5.
- Claude Fable 5 accepts more context: 1M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| Claude Fable 5 | GLM-4.6 | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 66.8 | 41.4 |
| Released | 2026-06-07 | 2025-09-30 |
| Weights | Proprietary | Open |
| Context window | 1M | 205K |
| Max output | 128K | 131K |
| Input $ / M tokens | $10 | $0.60 |
| Output $ / M tokens | $50 | $2.20 |
| Results tracked | 62 | 29 |
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Category by category
Coding Claude Fable 5 leads
Claude Fable 5: 70.6 (#4), GLM-4.6: 40.1 (#148)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena WebDev | 1625 | 1340 |
| SciCode | 61% | 38.4% |
| LMArena Coding | 1519 | 1449 |
| ALE-Bench | 2,041 | 340.82 |
| DeepSWE | 69.9% | — |
| FrontierCode | 53.5% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| FrontierSWE | 47% | — |
| GSO | 78.4% | — |
| WeirdML | 91.9% | — |
| MirrorCode | 63.9% | — |
Agentic & Tool Use Claude Fable 5 leads
Claude Fable 5: 54.0 (#2), GLM-4.6: 32.3 (#66)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| Terminal-Bench | — | 24.5% |
| APEX-Agents | 63.6% | — |
| Berkeley Function Calling Leaderboard | — | 72.4% |
| Remote Labor Index | 16.1% | — |
| τ²-bench Banking | 39.7% | — |
| PostTrainBench | 41.8% | — |
| GBAEval | 74.5% | — |
| GDP.pdf | 30% | — |
| LMArena Search | 1230 | — |
| Vending-Bench 2 | 5,680 | — |
Reasoning Claude Fable 5 leads
Claude Fable 5: 76.8 (#6), GLM-4.6: 23.7 (#172)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| Kagi LLM Benchmark | 91.4% | 47.4% |
| CritPt | 28.6% | 1.1% |
| LMArena Hard Prompts | 1508 | 1440 |
| ARC-AGI-2 | 89.2% | — |
| SimpleBench | 81.9% | — |
| NYT Connections (extended) | 92.7% | — |
| ARC-AGI-1 | 98.5% | — |
| Chess Puzzles | 41% | — |
| EnigmaEval | 39.3% | — |
| EBR-Bench | 39.5% | — |
| Mystery Game Puzzles | 52% | — |
| DTBench | 98.4% | — |
| LMCA | 61.1% | — |
| Surface Evolver Bench | 95% | — |
| Bench to the Future 3 | 0.13 | — |
| Epoch Capabilities Index | 162.06 | — |
Math Claude Fable 5 leads
Claude Fable 5: 88.5 (#5), GLM-4.6: 39.1 (#111)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Math | 1519 | 1432 |
| FrontierMath (Tiers 1-3) | 87% | — |
| FrontierMath Tier 4 | 90.2% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 95% | — |
| FrontierMath (Feb 2025 set) | — | 3.8% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge Claude Fable 5 leads
Claude Fable 5: 62.2 (#25), GLM-4.6: 40.2 (#124)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Expert | 1534 | 1431 |
| GPQA Diamond | 85.9% | — |
| SimpleQA Verified | 70.7% | — |
| Vectara Hallucination Rate | — | 9.5% |
Multimodal Not comparable
Claude Fable 5: 45.3 (#17), GLM-4.6: —
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Vision | 1324 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 35.8% | — |
| LMArena Document | 1496 | — |
Multilingual Claude Fable 5 leads
Claude Fable 5: 57.3 (#9), GLM-4.6: 53.5 (#66)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Non-English | 1481 | 1426 |
| LMArena Chinese | 1543 | 1499 |
| LMArena French | 1505 | 1459 |
| LMArena German | 1486 | 1447 |
| LMArena Japanese | 1506 | 1393 |
| LMArena Korean | 1488 | 1400 |
| LMArena Russian | 1504 | 1419 |
| LMArena Spanish | 1498 | 1436 |
Instruction Following Claude Fable 5 leads
Claude Fable 5: 78.6 (#8), GLM-4.6: 74.3 (#98)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Instruction Following | 1502 | 1410 |
Long Context Claude Fable 5 leads
Claude Fable 5: 46.3 (#23), GLM-4.6: 43.4 (#94)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Longer Query | 1509 | 1422 |
Writing & Preference Claude Fable 5 leads
Claude Fable 5: 75.9 (#5), GLM-4.6: 61.1 (#90)
| Benchmark | Claude Fable 5 | GLM-4.6 |
|---|---|---|
| LMArena Text | 1491 | 1440 |
| LMArena Creative Writing | 1494 | 1411 |
| EQ-Bench Creative Writing | 1943 | 1411 |
| LMArena Multi-Turn | 1504 | 1427 |
| EQ-Bench 4 | 1340 | — |
Frequently asked questions
Is Claude Fable 5 better than GLM-4.6?
Claude Fable 5 is the stronger model overall, scoring 66.8 to 41.4 on the Noometry Index. GLM-4.6 costs 20× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.
Which is cheaper, Claude Fable 5 or GLM-4.6?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Claude Fable 5 lists at $10 and $50.
Is Claude Fable 5 or GLM-4.6 better for coding?
Claude Fable 5 scores higher on coding benchmarks: 70.6 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Claude Fable 5 does, with 1M tokens against 205K.
How many benchmarks do Claude Fable 5 and GLM-4.6 share?
23 benchmarks have published results for both models. Claude Fable 5 has 62 scored results on Noometry and GLM-4.6 has 29.