Model comparison
GLM-5 vs GPT-6.1 Sol
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 46.1 on the Noometry Index. GLM-5 costs 2.6× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GLM-5 scores higher in 1 category and GPT-6.1 Sol in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6.1 Sol leads 81.9 to 27.6.
- The biggest single-benchmark swing is ARC-AGI-2: 4.9% for GLM-5 and 94.2% for GPT-6.1 Sol.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | GPT-6.1 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 46.1 | 65.6 |
| Released | 2026-02-11 | 2026-09-29 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $1 | $2 |
| Output $ / M tokens | $3.20 | $10 |
| Results tracked | 45 | 34 |
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Category by category
Coding GPT-6.1 Sol leads
GLM-5: 49.0 (#52), GPT-6.1 Sol: 63.2 (#8)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| LMArena WebDev | 1434 | 1755 |
| LMArena Coding | 1461 | 1487 |
| SWE-bench Verified | 72.1% | — |
| DeepSWE | — | 75.2% |
| FrontierCode | — | 50.2% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 55.8% |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use GPT-6.1 Sol leads
GLM-5: 31.1 (#71), GPT-6.1 Sol: 39.6 (#26)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 60% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| GDP.pdf | — | 32% |
| Vending-Bench 2 | 4,432 | — |
Reasoning GPT-6.1 Sol leads
GLM-5: 27.6 (#116), GPT-6.1 Sol: 81.9 (#2)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| ARC-AGI-2 | 4.9% | 94.2% |
| NYT Connections (extended) | 74.8% | 95.5% |
| ARC-AGI-1 | 44.7% | 98.5% |
| Chess Puzzles | 10% | 61% |
| LMArena Hard Prompts | 1452 | 1466 |
| Epoch Capabilities Index | 145.83 | 166.09 |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| CritPt | — | 31.7% |
| EBR-Bench | — | 54.3% |
| Mystery Game Puzzles | — | 80% |
| ForecastBench | 61 | — |
Math GPT-6.1 Sol leads
GLM-5: 46.4 (#71), GPT-6.1 Sol: 93.7 (#1)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 100% |
| LMArena Math | 1440 | 1464 |
| FrontierMath (Tiers 1-3) | — | 93.7% |
| FrontierMath Tier 4 | — | 100% |
| MathArena Final-Answer Competitions | 65.7% | — |
| ProofBench | — | 99% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-6.1 Sol leads
GLM-5: 52.3 (#64), GPT-6.1 Sol: 71.8 (#4)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| GPQA Diamond | 87.8% | 95.4% |
| LMArena Expert | 1454 | 1502 |
| SimpleQA Verified | — | 73.9% |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, GPT-6.1 Sol: 52.7 (#5)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| LMArena Vision | — | 1288 |
| Furniture Assembly | — | 80% |
Multilingual Too close to call
GLM-5: 53.7 (#58), GPT-6.1 Sol: 54.3 (#46)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1430 | 1438 |
| LMArena Chinese | 1511 | 1477 |
| LMArena Russian | 1436 | 1455 |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
| LMArena Spanish | 1454 | — |
Instruction Following GPT-6.1 Sol leads
GLM-5: 75.2 (#67), GPT-6.1 Sol: 77.0 (#29)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| LMArena Instruction Following | 1428 | 1468 |
Long Context Too close to call
GLM-5: 44.7 (#60), GPT-6.1 Sol: 44.9 (#54)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| LMArena Longer Query | 1446 | 1465 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), GPT-6.1 Sol: 63.6 (#63)
| Benchmark | GLM-5 | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1446 | 1447 |
| LMArena Creative Writing | 1439 | 1432 |
| LMArena Multi-Turn | 1456 | 1449 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than GPT-6.1 Sol?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 46.1 on the Noometry Index. GLM-5 costs 2.6× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GLM-5 or GPT-6.1 Sol?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GLM-5 or GPT-6.1 Sol better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 205K.
How many benchmarks do GLM-5 and GPT-6.1 Sol share?
20 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and GPT-6.1 Sol has 34.