Model comparison
GLM-4.6 vs Sonar
GLM-4.6 is the stronger model overall, scoring 41.4 to 38.5 on the Noometry Index.
Last verified . 0 shared benchmarks.
PerplexitySummary
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 52.6.
- Both cost about the same: $0.60 input and $2.20 output per million tokens.
- GLM-4.6 accepts more context: 205K tokens versus 128K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | Sonar | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Perplexity |
| Noometry Index | 41.4 | 38.5 |
| Released | 2025-09-30 | 2024-01-01 |
| Weights | Open | Proprietary |
| Context window | 205K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.60 | $1 |
| Output $ / M tokens | $2.20 | $1 |
| Results tracked | 29 | 7 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), Sonar: 35.7 (#221)
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| LiveBench Coding | — | 35.1% |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), Sonar: —
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), Sonar: 21.1 (#227)
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| LiveBench Reasoning | — | 46.3% |
| LMArena Hard Prompts | 1440 | — |
| LiveBench Data Analysis | — | 37.9% |
| LiveBench | — | 46.9% |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), Sonar: 33.7 (#200)
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| LiveBench Math | — | 41.6% |
| LMArena Math | 1432 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
GLM-4.6: 40.2 (#124), Sonar: —
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
Multilingual Not comparable
GLM-4.6: 53.5 (#66), Sonar: —
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), Sonar: 71.4 (#150)
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| LiveBench Instruction Following | — | 76.2% |
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#94), Sonar: —
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| LMArena Longer Query | 1422 | — |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), Sonar: 52.6 (#167)
| Benchmark | GLM-4.6 | Sonar |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
| LiveBench Language | — | 44.1% |
Frequently asked questions
Is GLM-4.6 better than Sonar?
GLM-4.6 is the stronger model overall, scoring 41.4 to 38.5 on the Noometry Index.
Which is cheaper, GLM-4.6 or Sonar?
Sonar is cheaper. It lists at $1 per million input tokens and $1 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or Sonar better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 35.7 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.6 and Sonar share?
0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Sonar has 7.