Model comparison
Gemini 2.5 Flash vs GLM-4.6V
GLM-4.6V is the stronger model overall, scoring 40.9 to 39.0 on the Noometry Index.
Last verified . 12 shared benchmarks.
GoogleSummary
- They share 12 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 4 categories and GLM-4.6V in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.6V leads 26.5 to 18.2.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.30 / $2.50 for Gemini 2.5 Flash.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 128K.
- GLM-4.6V has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash | GLM-4.6V | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 39.0 | 40.9 |
| Released | 2025-04-17 | 2025-12-08 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 66K | 33K |
| Input $ / M tokens | $0.30 | $0.30 |
| Output $ / M tokens | $2.50 | $0.90 |
| Results tracked | 54 | 12 |
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Category by category
Coding GLM-4.6V leads
Gemini 2.5 Flash: 35.5 (#210), GLM-4.6V: 40.2 (#128)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Coding | 1424 | 1390 |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| WeirdML | 41.9% | — |
| ALE-Bench | 661.88 | — |
Agentic & Tool Use Not comparable
Gemini 2.5 Flash: 30.6 (#67), GLM-4.6V: —
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| Terminal-Bench | 17.1% | — |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| Vending-Bench 2 | 548.84 | — |
Reasoning GLM-4.6V leads
Gemini 2.5 Flash: 18.2 (#271), GLM-4.6V: 26.5 (#117)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1368 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| CritPt | 1.1% | — |
| EnigmaEval | 2.7% | — |
| DTBench | 76.5% | — |
| LMCA | 27.5% | — |
| Epoch Capabilities Index | 143.04 | — |
| ForecastBench | 60.6 | — |
Math Not comparable
Gemini 2.5 Flash: 38.1 (#87), GLM-4.6V: —
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 73.1% | — |
| Omni-MATH | 38.5% | — |
| LMArena Math | 1415 | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GLM-4.6V leads
Gemini 2.5 Flash: 36.1 (#162), GLM-4.6V: 37.4 (#148)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Expert | 1426 | 1372 |
| Humanity's Last Exam | 12.1% | — |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |
Multimodal Gemini 2.5 Flash leads
Gemini 2.5 Flash: 41.2 (#31), GLM-4.6V: 34.2 (#83)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Vision | 1254 | 1164 |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| SpatialViz-Bench | 36.9% | — |
Multilingual Gemini 2.5 Flash leads
Gemini 2.5 Flash: 52.5 (#83), GLM-4.6V: 48.8 (#135)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Non-English | 1410 | 1359 |
| LMArena Chinese | 1450 | 1426 |
| LMArena Russian | 1415 | 1341 |
| LMArena French | 1433 | — |
| LMArena German | 1419 | — |
| LMArena Japanese | 1403 | — |
| LMArena Korean | 1385 | — |
| LMArena Spanish | 1421 | — |
Instruction Following Gemini 2.5 Flash leads
Gemini 2.5 Flash: 75.9 (#50), GLM-4.6V: 71.6 (#145)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Instruction Following | 1405 | 1351 |
| IFEval | 89.8% | — |
Long Context Gemini 2.5 Flash leads
Gemini 2.5 Flash: 47.5 (#16), GLM-4.6V: 41.3 (#138)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Longer Query | 1419 | 1358 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference GLM-4.6V leads
Gemini 2.5 Flash: 54.0 (#153), GLM-4.6V: 56.8 (#132)
| Benchmark | Gemini 2.5 Flash | GLM-4.6V |
|---|---|---|
| LMArena Text | 1417 | 1377 |
| LMArena Creative Writing | 1400 | 1347 |
| LMArena Multi-Turn | 1408 | 1359 |
| Short-Story Creative Writing | 76.5% | — |
| EQ-Bench Creative Writing | 1137 | — |
| WildBench | 81.7% | — |
Frequently asked questions
Is Gemini 2.5 Flash better than GLM-4.6V?
GLM-4.6V is the stronger model overall, scoring 40.9 to 39.0 on the Noometry Index.
Which is cheaper, Gemini 2.5 Flash or GLM-4.6V?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Gemini 2.5 Flash lists at $0.30 and $2.50.
Is Gemini 2.5 Flash or GLM-4.6V better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 35.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash does, with 1.05M tokens against 128K.
How many benchmarks do Gemini 2.5 Flash and GLM-4.6V share?
12 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and GLM-4.6V has 12.