Model comparison
Gemini 2.5 Flash-Lite vs GLM-5
GLM-5 is the stronger model overall, scoring 46.1 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 8.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
GoogleSummary
- They share 22 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 0 categories and GLM-5 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.3 to 32.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 40.5% for Gemini 2.5 Flash-Lite and 75% for GLM-5.
- Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $1 / $3.20 for GLM-5.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash-Lite | GLM-5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | |
| Noometry Index | 37.0 | 46.1 |
| Released | 2025-06-17 | 2026-02-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 205K |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.10 | $1 |
| Output $ / M tokens | $0.40 | $3.20 |
| Results tracked | 33 | 45 |
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Category by category
Coding GLM-5 leads
Gemini 2.5 Flash-Lite: 38.5 (#173), GLM-5: 49.0 (#52)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| WeirdML | 35.2% | 48.2% |
| LMArena Coding | 1373 | 1461 |
| ALE-Bench | 325.9 | 765.62 |
| SWE-bench Verified | — | 72.1% |
| SWE-bench Verified (bash only) | — | 72.8% |
| LMArena WebDev | — | 1434 |
| SWE-bench Multilingual | — | 69.7% |
Agentic & Tool Use GLM-5 leads
Gemini 2.5 Flash-Lite: 28.0 (#96), GLM-5: 31.1 (#71)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| Terminal-Bench | — | 52.4% |
| Berkeley Function Calling Leaderboard | 36.9% | — |
| τ²-bench Airline | — | 82.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 73.7% |
| τ²-bench Telecom | — | 86.8% |
| Vending-Bench 2 | — | 4,432 |
Reasoning GLM-5 leads
Gemini 2.5 Flash-Lite: 22.2 (#205), GLM-5: 27.6 (#116)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| Kagi LLM Benchmark | 40.5% | 75% |
| LMArena Hard Prompts | 1377 | 1452 |
| Epoch Capabilities Index | 133.94 | 145.83 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | — | 53.2% |
| NYT Connections (extended) | — | 74.8% |
| ARC-AGI-1 | — | 44.7% |
| Chess Puzzles | — | 10% |
| DTBench | 62.8% | — |
| LMCA | 18.1% | — |
| ForecastBench | — | 61 |
Math GLM-5 leads
Gemini 2.5 Flash-Lite: 38.0 (#144), GLM-5: 46.4 (#71)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| LMArena Math | 1373 | 1440 |
| MathArena Final-Answer Competitions | — | 65.7% |
| OTIS Mock AIME 2024-2025 | — | 80% |
| Omni-MATH | 48% | — |
| FrontierMath (Feb 2025 set) | — | 16.4% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GLM-5 leads
Gemini 2.5 Flash-Lite: 32.5 (#210), GLM-5: 52.3 (#64)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| Vectara Hallucination Rate | 3.3% | 10.1% |
| LMArena Expert | 1373 | 1454 |
| GPQA Diamond | — | 87.8% |
| MMLU-Pro | 53.7% | — |
| GPQA (HELM) | 30.9% | — |
Multimodal Not comparable
Gemini 2.5 Flash-Lite: 29.1 (#114), GLM-5: —
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |
Multilingual GLM-5 leads
Gemini 2.5 Flash-Lite: 49.3 (#134), GLM-5: 53.7 (#58)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| LMArena Non-English | 1369 | 1430 |
| LMArena Chinese | 1404 | 1511 |
| LMArena French | 1388 | 1455 |
| LMArena German | 1389 | 1445 |
| LMArena Japanese | 1359 | 1416 |
| LMArena Korean | 1360 | 1423 |
| LMArena Russian | 1373 | 1436 |
| LMArena Spanish | 1396 | 1454 |
Instruction Following GLM-5 leads
Gemini 2.5 Flash-Lite: 70.0 (#168), GLM-5: 75.2 (#67)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| LMArena Instruction Following | 1367 | 1428 |
| IFEval | 81% | — |
Long Context GLM-5 leads
Gemini 2.5 Flash-Lite: 33.3 (#262), GLM-5: 44.7 (#60)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| LMArena Longer Query | 1373 | 1446 |
| Fiction.LiveBench | 47.2% | — |
| CL-bench | — | 18.7% |
Writing & Preference GLM-5 leads
Gemini 2.5 Flash-Lite: 56.8 (#135), GLM-5: 66.0 (#38)
| Benchmark | Gemini 2.5 Flash-Lite | GLM-5 |
|---|---|---|
| LMArena Text | 1379 | 1446 |
| LMArena Creative Writing | 1367 | 1439 |
| LMArena Multi-Turn | 1366 | 1456 |
| EQ-Bench Creative Writing | — | 1601 |
| WildBench | 81.8% | — |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than GLM-5?
GLM-5 is the stronger model overall, scoring 46.1 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 8.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, Gemini 2.5 Flash-Lite or GLM-5?
Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GLM-5 lists at $1 and $3.20.
Is Gemini 2.5 Flash-Lite or GLM-5 better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 38.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 205K.
How many benchmarks do Gemini 2.5 Flash-Lite and GLM-5 share?
22 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and GLM-5 has 45.