See key specs and per-benchmark scores for each model/mode. Scroll horizontally for all columns. 当前对比 2 个模型的评测数据与核心参数。

Gemma 4 31B
DeepMind
Each axis is a category average, normalized to a 100-point radar.
Relative edge: none clear / Relative gap: Agent能力评测 -12.8
Relative edge: Agent能力评测 +12.8 / Relative gap: none clear
Method: for each model and benchmark, the chart first averages all scores in the current mode scope instead of taking the best score, then averages those benchmark scores within each category. Only benchmarks with at least two selected models scored are included; missing values are not counted as zero.
Best overall
GLM-5 · 79.70
Best single
GLM-5 · AIME 2026 92.70
Modality coverage
Gemma 4 31B · 3 modalities
Head to head
4
Benchmarks
0
Wins
4
Losses
-10.47
Average diff
Compare benchmark results across thinking modes and tool usage.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Complete scores for each model/mode across selected benchmarks.
4 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | Gemma 4 31B | GLM-5 |
|---|---|---|
GPQA Diamond 综合评估 | 84.30Thinking Enabled | 86.00Thinking Enabled |
HLE 综合评估 | 26.50Thinking Enabled | Tools | 50.40Thinking Enabled | Tools |
τ²-Bench Agent能力评测 | 76.90Thinking Enabled | Tools | 89.70Thinking Enabled | Tools |
AIME 2026 数学推理 | 89.20Thinking Enabled | 92.70Thinking Enabled |
Side-by-side input/output token pricing
Licensing, MoE architecture, and multi-modality support.
| Features & specs | Gemma 4 31BDeepMind | GLM-5智谱AI |
|---|---|---|
Core specsRelease | 2026-04-02 | 2026-02-11 |
Context length | 256K | 200K |
Parameters | 31 | 7440 |
Active parameters | 31 | 400 |
Max output | 32768 | 131072 |
MoE | No | Yes |
LicenseCode Open Source | Not provided | Not provided |
Weights Open Source | Not provided | Closed Source |
Commercial use | 免费商用授权 | 免费商用授权 |
Modality supportText Input/Output | / | / |
Image Input/Output | / | Not provided |
Video Input/Output | / | Not provided |
ResourcesPaper / report | Gemma 4: Byte for byte, the most capable open models | GLM-5: From Vibe Coding to Agentic Engineering |
DataLearner blog | Google Gemma 4 正式开源:Apache 2.0 协议、手机端可运行、原生支持多模态和 Agent 工作流 | Not provided |

GLM-5
智谱AI