Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 Speciale vs Qwen3.7 Max
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.7 Max wins on 3/3 benchmarks
Qwen3.7 Max wins 3 of 3 shared benchmarks. Leads in speed.
Category leads
speed·Qwen3.7 Max
Hype vs Reality
Attention vs performance
DeepSeek V3.2 Speciale
#6 by perf·#5 by attention
Qwen3.7 Max
#28 by perf·no signal
Best value
DeepSeek V3.2 Speciale
3.6x better value than Qwen3.7 Max
DeepSeek V3.2 Speciale
97.8 pts/$
$0.80/M
Qwen3.7 Max
27.2 pts/$
$2.50/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
3 benchmarks · 2 models
DeepSeek V3.2 SpecialeQwen3.7 Max
Artificial Analysis · Agentic Index
Qwen3.7 Max leads by +30.6
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?"
DeepSeek V3.2 Speciale
0.0
Qwen3.7 Max
30.6
Artificial Analysis · Coding Index
Qwen3.7 Max leads by +28.1
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.
DeepSeek V3.2 Speciale
37.9
Qwen3.7 Max
66.0
Artificial Analysis · Quality Index
Qwen3.7 Max leads by +16.6
DeepSeek V3.2 Speciale
29.4
Qwen3.7 Max
46.0
Full benchmark table
| Benchmark | DeepSeek V3.2 Speciale | Qwen3.7 Max |
|---|---|---|
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?" | 0.0 | 30.6 |
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. | 37.9 | 66.0 |
Artificial Analysis · Quality Index | 29.4 | 46.0 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.40 | $1.20 | 164K tokens (~82 books) | $6.00 | |
| $1.25 | $3.75 | 1.0M tokens (~500 books) | $18.75 |
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