Model comparison
GLM-5.2 vs Grok 4.1 Fast
GLM-5.2 is the stronger model overall, scoring 51.1 to 41.4 on the Noometry Index. Grok 4.1 Fast costs 7.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. GLM-5.2 scores higher in 7 categories and Grok 4.1 Fast in 2 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 33.1.
- The biggest single-benchmark swing is ProofBench: 35% for GLM-5.2 and 4% for Grok 4.1 Fast.
- Grok 4.1 Fast is cheaper at $0.20 / $0.50 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 128K.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | Grok 4.1 Fast | |
|---|---|---|
| Provider | Z.ai (Zhipu) | xAI |
| Noometry Index | 51.1 | 41.4 |
| Released | 2026-06-13 | 2025-06-27 |
| Weights | Open | Proprietary |
| Context window | 1M | 128K |
| Max output | 131K | 30K |
| Input $ / M tokens | $1.40 | $0.20 |
| Output $ / M tokens | $4.40 | $0.50 |
| Results tracked | 51 | 32 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Grok 4.1 Fast: 34.1 (#245)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena WebDev | 1603 | 1242 |
| LMArena Coding | 1485 | 1411 |
| ALE-Bench | 1,047 | 394.93 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
Agentic & Tool Use Grok 4.1 Fast leads
GLM-5.2: 32.4 (#63), Grok 4.1 Fast: 36.3 (#39)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| τ²-bench Banking | 37.1% | 13.1% |
| Vending-Bench 2 | 8,314 | 1,107 |
| APEX-Agents | 45.2% | — |
| Berkeley Function Calling Leaderboard | — | 69.6% |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| LMArena Search | — | 1171 |
Reasoning Grok 4.1 Fast leads
GLM-5.2: 42.3 (#52), Grok 4.1 Fast: 43.4 (#49)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| SimpleBench | 58.8% | 56% |
| NYT Connections (extended) | 74.3% | 87.4% |
| LMArena Hard Prompts | 1480 | 1407 |
| DTBench | 93.6% | 87.7% |
| ARC-AGI-2 | 22.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |
| ForecastBench | — | 61 |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Grok 4.1 Fast: 31.9 (#221)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| MathArena Final-Answer Competitions | 67.6% | 60.9% |
| ProofBench | 35% | 4% |
| LMArena Math | 1482 | 1408 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Grok 4.1 Fast: 33.1 (#207)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena Expert | 1486 | 1399 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 17.8% |
Multimodal Not comparable
GLM-5.2: —, Grok 4.1 Fast: 37.0 (#76)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena Vision | — | 1201 |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Grok 4.1 Fast: 51.0 (#114)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena Non-English | 1459 | 1391 |
| LMArena Chinese | 1519 | 1441 |
| LMArena French | 1479 | 1415 |
| LMArena German | 1468 | 1404 |
| LMArena Japanese | 1451 | 1349 |
| LMArena Korean | 1445 | 1361 |
| LMArena Russian | 1466 | 1387 |
| LMArena Spanish | 1477 | 1413 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Grok 4.1 Fast: 72.7 (#133)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena Instruction Following | 1465 | 1376 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Grok 4.1 Fast: 42.4 (#126)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena Longer Query | 1479 | 1390 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Grok 4.1 Fast: 57.2 (#131)
| Benchmark | GLM-5.2 | Grok 4.1 Fast |
|---|---|---|
| LMArena Text | 1470 | 1408 |
| LMArena Creative Writing | 1462 | 1394 |
| EQ-Bench Creative Writing | 1757 | 1327 |
| LMArena Multi-Turn | 1469 | 1389 |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Grok 4.1 Fast?
GLM-5.2 is the stronger model overall, scoring 51.1 to 41.4 on the Noometry Index. Grok 4.1 Fast costs 7.8× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Grok 4.1 Fast?
Grok 4.1 Fast is cheaper. It lists at $0.20 per million input tokens and $0.50 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Grok 4.1 Fast better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.2 and Grok 4.1 Fast share?
27 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Grok 4.1 Fast has 32.