Model comparison
GLM-4.7-Flash vs GPT-5 Nano
GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 33.4 on the Noometry Index.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GLM-4.7-Flash scores higher in 6 categories and GPT-5 Nano in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where GLM-4.7-Flash leads 40.9 to 31.3.
- The biggest single-benchmark swing is Chess Puzzles: 0% for GLM-4.7-Flash and 27% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.06 / $0.40 for GLM-4.7-Flash.
- GPT-5 Nano accepts more context: 400K tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | GPT-5 Nano | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 33.4 |
| Released | 2026-01-19 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 200K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.06 | $0.05 |
| Output $ / M tokens | $0.40 | $0.40 |
| Results tracked | 21 | 48 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.0 (#133), GPT-5 Nano: 33.3 (#248)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Coding | 1383 | 1351 |
| SWE-bench Verified (bash only) | — | 34.8% |
| WeirdML | — | 38.1% |
| ALE-Bench | — | 718.67 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, GPT-5 Nano: 25.6 (#99)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |
Reasoning GLM-4.7-Flash leads
GLM-4.7-Flash: 20.2 (#224), GPT-5 Nano: 16.4 (#291)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| Chess Puzzles | 0% | 27% |
| LMArena Hard Prompts | 1356 | 1329 |
| ARC-AGI-2 | — | 2.6% |
| Kagi LLM Benchmark | — | 62.2% |
| ARC-AGI-1 | — | 20.7% |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 62.7% |
| LMCA | — | 7.9% |
| Epoch Capabilities Index | — | 139.39 |
| ForecastBench | — | 59.1 |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 34.4 (#159), GPT-5 Nano: 29.1 (#221)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 81.1% |
| LMArena Math | 1354 | 1318 |
| FrontierMath (Tiers 1-3) | — | 20% |
| FrontierMath Tier 4 | — | 2.4% |
| ProofBench | — | 12% |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge Too close to call
GLM-4.7-Flash: 35.4 (#177), GPT-5 Nano: 35.8 (#166)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 60.5% | 69.4% |
| Vectara Hallucination Rate | 9.3% | 10.5% |
| LMArena Expert | 1357 | 1321 |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| GPQA (HELM) | — | 67.9% |
Multimodal Not comparable
GLM-4.7-Flash: —, GPT-5 Nano: 30.7 (#102)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.8 (#152), GPT-5 Nano: 45.5 (#167)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1331 | 1313 |
| LMArena Chinese | 1403 | 1355 |
| LMArena German | 1336 | 1329 |
| LMArena Korean | 1284 | 1269 |
| LMArena Russian | 1333 | 1296 |
| LMArena Spanish | 1351 | 1360 |
| LMArena French | 1332 | — |
Instruction Following GPT-5 Nano leads
GLM-4.7-Flash: 70.3 (#162), GPT-5 Nano: 75.2 (#72)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1327 | 1306 |
| IFEval | — | 93.2% |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#143), GPT-5 Nano: 31.3 (#275)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1345 | 1313 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.7 (#206), GPT-5 Nano: 39.4 (#244)
| Benchmark | GLM-4.7-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1351 | 1320 |
| LMArena Creative Writing | 1297 | 1250 |
| EQ-Bench Creative Writing | 1125 | 705 |
| LMArena Multi-Turn | 1342 | 1311 |
| WildBench | — | 80.6% |
Frequently asked questions
Is GLM-4.7-Flash better than GPT-5 Nano?
GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 33.4 on the Noometry Index.
Which is cheaper, GLM-4.7-Flash or GPT-5 Nano?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; GLM-4.7-Flash lists at $0.06 and $0.40.
Is GLM-4.7-Flash or GPT-5 Nano better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 33.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and GPT-5 Nano share?
20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5 Nano has 48.