Model comparison
GLM-4.5V vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.8 on the Noometry Index. GLM-4.5V costs 8.9× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Summary
- They share 15 benchmarks with published results for both. GLM-4.5V scores higher in 0 categories and GPT-5.6 Sol in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 37.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 67% for GPT-5.6 Sol.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.5V | GPT-5.6 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 39.8 | 65.0 |
| Released | 2025-08-11 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 64K | 1.05M |
| Max output | 16K | 128K |
| Input $ / M tokens | $0.60 | $4 |
| Output $ / M tokens | $1.80 | $20 |
| Results tracked | 15 | 65 |
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Category by category
Coding GPT-5.6 Sol leads
GLM-4.5V: 39.5 (#155), GPT-5.6 Sol: 65.1 (#7)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Coding | 1347 | 1498 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| LMArena WebDev | — | 1618 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| GSO | — | 76.5% |
| WeirdML | — | 89.4% |
| MirrorCode | — | 20% |
| ALE-Bench | — | 2,177 |
Agentic & Tool Use Not comparable
GLM-4.5V: —, GPT-5.6 Sol: 50.3 (#7)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | — | 51.4% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |
Reasoning GPT-5.6 Sol leads
GLM-4.5V: 27.4 (#119), GPT-5.6 Sol: 74.8 (#8)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 67% |
| LMArena Hard Prompts | 1334 | 1484 |
| ARC-AGI-2 | — | 92.5% |
| SimpleBench | — | 71.7% |
| NYT Connections (extended) | — | 93.8% |
| ARC-AGI-1 | — | 97.5% |
| CritPt | — | 32.3% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| Mystery Game Puzzles | — | 58% |
| DTBench | — | 96% |
| LMCA | — | 59.2% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 161.66 |
Math GPT-5.6 Sol leads
GLM-4.5V: 37.4 (#159), GPT-5.6 Sol: 85.6 (#9)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Math | 1354 | 1474 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 83% |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.6 Sol leads
GLM-4.5V: 37.5 (#156), GPT-5.6 Sol: 64.3 (#18)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Expert | 1353 | 1516 |
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
| Vectara Hallucination Rate | — | 12.4% |
Multimodal GPT-5.6 Sol leads
GLM-4.5V: 34.3 (#92), GPT-5.6 Sol: 48.6 (#9)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | 1154 | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual GPT-5.6 Sol leads
GLM-4.5V: 44.6 (#177), GPT-5.6 Sol: 55.3 (#32)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1303 | 1452 |
| LMArena Chinese | 1337 | 1527 |
| LMArena Russian | 1298 | 1468 |
| LMArena Spanish | 1336 | 1441 |
| LMArena French | — | 1477 |
| LMArena German | — | 1476 |
| LMArena Japanese | — | 1471 |
| LMArena Korean | — | 1442 |
Instruction Following GPT-5.6 Sol leads
GLM-4.5V: 69.2 (#175), GPT-5.6 Sol: 77.7 (#16)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1311 | 1482 |
Long Context GPT-5.6 Sol leads
GLM-4.5V: 39.6 (#171), GPT-5.6 Sol: 45.4 (#42)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1304 | 1480 |
Writing & Preference GPT-5.6 Sol leads
GLM-4.5V: 52.5 (#170), GPT-5.6 Sol: 73.3 (#12)
| Benchmark | GLM-4.5V | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1333 | 1457 |
| LMArena Creative Writing | 1295 | 1448 |
| LMArena Multi-Turn | 1332 | 1460 |
| EQ-Bench Creative Writing | — | 1972 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is GLM-4.5V better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.8 on the Noometry Index. GLM-4.5V costs 8.9× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5V or GPT-5.6 Sol?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GLM-4.5V or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 64K.
How many benchmarks do GLM-4.5V and GPT-5.6 Sol share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and GPT-5.6 Sol has 65.