Compare · ModelsLive · 2 picked · head to head
Gemini 2.5 Pro vs Qwen3 Next 80B A3B Instruct
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Gemini 2.5 Pro wins on 10/10 benchmarks
Gemini 2.5 Pro wins 10 of 10 shared benchmarks. Leads in speed · arena · math.
Category leads
speed·Gemini 2.5 Proarena·Gemini 2.5 Promath·Gemini 2.5 Proknowledge·Gemini 2.5 Prolanguage·Gemini 2.5 Procoding·Gemini 2.5 Pro
Hype vs Reality
Attention vs performance
Gemini 2.5 Pro
#100 by perf·no signal
Qwen3 Next 80B A3B Instruct
#89 by perf·no signal
Best value
Qwen3 Next 80B A3B Instruct
9.8x better value than Gemini 2.5 Pro
Gemini 2.5 Pro
9.4 pts/$
$5.63/M
Qwen3 Next 80B A3B Instruct
91.4 pts/$
$0.60/M
Vendor risk
Who is behind the model
Google DeepMind
$4.00T·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
10 benchmarks · 2 models
Gemini 2.5 ProQwen3 Next 80B A3B Instruct
Artificial Analysis · Agentic Index
Gemini 2.5 Pro leads by +18.5
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Gemini 2.5 Pro
32.7
Qwen3 Next 80B A3B Instruct
14.2
Artificial Analysis · Coding Index
Gemini 2.5 Pro leads by +16.7
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Gemini 2.5 Pro
31.9
Qwen3 Next 80B A3B Instruct
15.3
Artificial Analysis · Quality Index
Gemini 2.5 Pro leads by +13.3
Gemini 2.5 Pro
27.0
Qwen3 Next 80B A3B Instruct
13.7
Chatbot Arena Elo · Overall
Gemini 2.5 Pro leads by +44.1
Gemini 2.5 Pro
1445.7
Qwen3 Next 80B A3B Instruct
1401.6
OpenCompass · AIME2025
Gemini 2.5 Pro leads by +19.5
Gemini 2.5 Pro
88.7
Qwen3 Next 80B A3B Instruct
69.2
OpenCompass · GPQA-Diamond
Gemini 2.5 Pro leads by +10.6
Gemini 2.5 Pro
84.7
Qwen3 Next 80B A3B Instruct
74.1
OpenCompass · HLE
Gemini 2.5 Pro leads by +13.1
Gemini 2.5 Pro
21.1
Qwen3 Next 80B A3B Instruct
8.0
OpenCompass · IFEval
Gemini 2.5 Pro leads by +2.4
Gemini 2.5 Pro
90.0
Qwen3 Next 80B A3B Instruct
87.6
OpenCompass · LiveCodeBenchV6
Gemini 2.5 Pro leads by +16.5
Gemini 2.5 Pro
71.3
Qwen3 Next 80B A3B Instruct
54.8
OpenCompass · MMLU-Pro
Gemini 2.5 Pro leads by +4.5
Gemini 2.5 Pro
85.8
Qwen3 Next 80B A3B Instruct
81.3
Full benchmark table
| Benchmark | Gemini 2.5 Pro | Qwen3 Next 80B A3B Instruct |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 32.7 | 14.2 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 31.9 | 15.3 |
Artificial Analysis · Quality Index | 27.0 | 13.7 |
Chatbot Arena Elo · Overall | 1445.7 | 1401.6 |
OpenCompass · AIME2025 | 88.7 | 69.2 |
OpenCompass · GPQA-Diamond | 84.7 | 74.1 |
OpenCompass · HLE | 21.1 | 8.0 |
OpenCompass · IFEval | 90.0 | 87.6 |
OpenCompass · LiveCodeBenchV6 | 71.3 | 54.8 |
OpenCompass · MMLU-Pro | 85.8 | 81.3 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.25 | $10.00 | 1.0M tokens (~524 books) | $34.38 | |
| $0.09 | $1.10 | 262K tokens (~131 books) | $3.43 |