Model comparison
GLM-4.7-Flash vs Kimi K2.6
Kimi K2.6 is the stronger model overall, scoring 47.7 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 12× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and Kimi K2.6 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2.6 leads 68.5 to 47.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 96.1% for Kimi K2.6.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 200K.
Side by side
| GLM-4.7-Flash | Kimi K2.6 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 38.8 | 47.7 |
| Released | 2026-01-19 | 2026-04-20 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 262K |
| Input $ / M tokens | $0.06 | $0.95 |
| Output $ / M tokens | $0.40 | $4 |
| Results tracked | 21 | 51 |
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Category by category
Coding Kimi K2.6 leads
GLM-4.7-Flash: 40.6 (#135), Kimi K2.6: 50.7 (#43)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| LMArena Coding | 1383 | 1488 |
| SWE-bench Verified | — | 76.7% |
| LMArena WebDev | — | 1509 |
| SciCode | — | 53.5% |
| WeirdML | — | 55.9% |
| ALE-Bench | — | 1,093 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Kimi K2.6: 21.9 (#137)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| OSWorld 2.0 | — | 4.6% |
| ExploitBench | — | 18.4% |
| GBAEval | — | 0.9% |
| GDP.pdf | — | 12% |
| Vending-Bench 2 | — | 6,205 |
Reasoning Kimi K2.6 leads
GLM-4.7-Flash: 20.9 (#229), Kimi K2.6: 40.5 (#55)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| Chess Puzzles | 0% | 26% |
| LMArena Hard Prompts | 1356 | 1470 |
| NYT Connections (extended) | — | 87.2% |
| CritPt | — | 8% |
| EBR-Bench | — | 2.4% |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 90.9% |
| LMCA | — | 37.3% |
| Epoch Capabilities Index | — | 151.05 |
Math Kimi K2.6 leads
GLM-4.7-Flash: 36.1 (#173), Kimi K2.6: 57.0 (#41)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 96.1% |
| LMArena Math | 1355 | 1475 |
| FrontierMath (Tiers 1-3) | — | 57.2% |
| FrontierMath Tier 4 | — | 25.6% |
| MathArena Final-Answer Competitions | — | 72.9% |
| ProofBench | — | 16% |
| FrontierMath (Feb 2025 set) | — | 39% |
| FrontierMath Tier 4 (v1) | — | 14.6% |
Knowledge Kimi K2.6 leads
GLM-4.7-Flash: 35.5 (#184), Kimi K2.6: 54.0 (#54)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| GPQA Diamond | 60.5% | 90.8% |
| Vectara Hallucination Rate | 9.3% | 10.8% |
| LMArena Expert | 1357 | 1491 |
| SimpleQA Verified | — | 34.9% |
Multimodal Not comparable
GLM-4.7-Flash: —, Kimi K2.6: 31.6 (#103)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| LMArena Vision | — | 1283 |
| Blueprint-Bench 2 | — | 3.9% |
| Furniture Assembly | — | 21.7% |
| LMArena Document | — | 1451 |
Multilingual Kimi K2.6 leads
GLM-4.7-Flash: 46.5 (#158), Kimi K2.6: 54.9 (#37)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | 1330 | 1446 |
| LMArena Chinese | 1403 | 1521 |
| LMArena French | 1332 | 1471 |
| LMArena German | 1337 | 1450 |
| LMArena Korean | 1283 | 1427 |
| LMArena Russian | 1332 | 1446 |
| LMArena Spanish | 1350 | 1464 |
| LMArena Japanese | — | 1443 |
Instruction Following Kimi K2.6 leads
GLM-4.7-Flash: 70.1 (#167), Kimi K2.6: 76.3 (#43)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1451 |
Long Context Kimi K2.6 leads
GLM-4.7-Flash: 40.9 (#148), Kimi K2.6: 44.9 (#52)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | 1345 | 1468 |
Writing & Preference Kimi K2.6 leads
GLM-4.7-Flash: 47.4 (#210), Kimi K2.6: 68.5 (#26)
| Benchmark | GLM-4.7-Flash | Kimi K2.6 |
|---|---|---|
| LMArena Text | 1351 | 1455 |
| LMArena Creative Writing | 1297 | 1434 |
| EQ-Bench Creative Writing | 1125 | 1725 |
| LMArena Multi-Turn | 1342 | 1453 |
| EQ-Bench 4 | — | 1202 |
Frequently asked questions
Is GLM-4.7-Flash better than Kimi K2.6?
Kimi K2.6 is the stronger model overall, scoring 47.7 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 12× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Kimi K2.6?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Kimi K2.6 lists at $0.95 and $4.
Is GLM-4.7-Flash or Kimi K2.6 better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.7 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.6 does, with 262K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Kimi K2.6 share?
21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Kimi K2.6 has 51.