Qwen3.6-27BvsGemini 3.0 Flash
Across 3 shared benchmarks, Qwen3.6-27B leads overall: Qwen3.6-27B wins 2, Gemini 3.0 Flash wins 1, with 0 ties and an average score difference of +2.03.
Qwen3.6-27B
阿里巴巴 · 2026-04-22 · Reasoning model
Gemini 3.0 Flash
Google Deep Mind · 2025-12-17 · Chat model
Qwen3.6-27B2 wins(67%)(33%)1 winGemini 3.0 Flash
Benchmark scores
Grouped by capability, sorted by largest gap within each. 3 shared benchmarks.
AI Agent - Tool Usage
Qwen3.6-27B 1/1| Benchmark | Qwen3.6-27B | Gemini 3.0 Flash | Diff |
|---|---|---|---|
| Terminal Bench 2.0 | 59.3020 / 48Thinking (With Tools) | 47.6039 / 48Thinking (With Tools) | +11.70 |
Claw-style Agent Evaluation
Gemini 3.0 Flash 1/1| Benchmark | Qwen3.6-27B | Gemini 3.0 Flash | Diff |
|---|---|---|---|
| Claw Bench | 72.4027 / 29Thinking (With Tools) | 85.7015 / 29Thinking (With Tools) | -13.30 |
General Knowledge
Qwen3.6-27B 1/1| Benchmark | Qwen3.6-27B | Gemini 3.0 Flash | Diff |
|---|---|---|---|
| LiveBench | 64.0354 / 117Normal (No Tools) | 56.3581 / 117Normal (No Tools) | +7.68 |
Specs
| Field | Qwen3.6-27B | Gemini 3.0 Flash |
|---|---|---|
| Publisher | 阿里巴巴 | Google Deep Mind |
| Release date | 2026-04-22 | 2025-12-17 |
| Model type | Reasoning model | Chat model |
| Architecture | Dense | Dense |
| Parameters | 27B | Not available |
| Context length | 128K | 2000K |
| Max output | 16K | 64K |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | Qwen3.6-27B | Gemini 3.0 Flash |
|---|---|---|
| Text input | Not public | $0.5 / 1M tokens |
| Text output | Not public | $3 / 1M tokens |
One or both models have incomplete public pricing.
Summary
- Qwen3.6-27Bleads in:AI Agent - Tool Usage (1/1), General Knowledge (1/1)
- Gemini 3.0 Flashleads in:Claw-style Agent Evaluation (1/1)
On average across the 3 shared benchmarks, Qwen3.6-27B scores 2.03 higher.
Largest single-benchmark gap: Claw Bench — Qwen3.6-27B 72.40 vs Gemini 3.0 Flash 85.70 (-13.30).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.