| Signal | DeepSeek V3.2 | Delta | Qwen3.5-122B-A10B |
|---|---|---|---|
Capabilities | 67 | -17 | |
Benchmarks | 70 | +1 | |
Pricing | 0 | -2 | |
Context window size | 83 | -3 | |
Recency | 100 | -- | |
Output Capacity | 20 | -60 | |
| Overall Result | 1 wins | of 6 | 4 wins |
7
days higher
6
days
17
days higher
DeepSeek
Alibaba
DeepSeek V3.2 saves you $85.00/month
That's $1020.00/year compared to Qwen3.5-122B-A10B at your current usage level of 100K calls/month.
| Metric | DeepSeek V3.2 | Qwen3.5-122B-A10B | Winner |
|---|---|---|---|
| Overall Score | 70 | 70 | -- |
| Rank | #58 | #57 | Qwen3.5-122B-A10B |
| Quality Rank | #58 | #57 | Qwen3.5-122B-A10B |
| Adoption Rank | #58 | #57 | Qwen3.5-122B-A10B |
| Parameters | -- | 122B | -- |
| Context Window | 164K | 262K | Qwen3.5-122B-A10B |
| Pricing | $0.26/$0.38/M | $0.26/$2.08/M | -- |
| Signal Scores | |||
| Capabilities | 67 | 83 | Qwen3.5-122B-A10B |
| Benchmarks | 70 | 69 | DeepSeek V3.2 |
| Pricing | 0 | 2 | Qwen3.5-122B-A10B |
| Context window size | 83 | 86 | Qwen3.5-122B-A10B |
| Recency | 100 | 100 | DeepSeek V3.2 |
| Output Capacity | 20 | 80 | Qwen3.5-122B-A10B |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Here's what the scores mean for these two models:
Scores 70/100 (rank #58), placing it in the top 80% of all 290 models tracked.
Scores 70/100 (rank #57), placing it in the top 81% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
DeepSeek V3.2 offers 73% better value per quality point. At 1M tokens/day, you'd spend $9.60/month with DeepSeek V3.2 vs $35.10/month with Qwen3.5-122B-A10B - a $25.50 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. DeepSeek V3.2 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.38/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (70/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
DeepSeek V3.2 and Qwen3.5-122B-A10B are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
DeepSeek V3.2
Marginally better benchmark scores; both are excellent
Best for Cost
DeepSeek V3.2
73% lower pricing; better value at scale
Best for Reliability
DeepSeek V3.2
Higher uptime and faster response speeds
Best for Prototyping
DeepSeek V3.2
Stronger community support and better developer experience
Best for Production
DeepSeek V3.2
Wider enterprise adoption and proven at scale
by DeepSeek
| Capability | DeepSeek V3.2 | Qwen3.5-122B-A10B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
DeepSeek
Alibaba
DeepSeek V3.2 saves you $2.04/month
That's 69% cheaper than Qwen3.5-122B-A10B at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | DeepSeek V3.2 | Qwen3.5-122B-A10B |
|---|---|---|
| Context Window | 164K | 262K |
| Max Output Tokens | -- | 65,536 |
| Open Source | Yes | Yes |
| Created | Dec 1, 2025 | Feb 25, 2026 |
Both DeepSeek V3.2 and Qwen3.5-122B-A10B score 70/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
DeepSeek V3.2 is ranked #58 and Qwen3.5-122B-A10B is ranked #57 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
DeepSeek V3.2 is cheaper at $0.38/M output tokens vs Qwen3.5-122B-A10B's $2.08/M output tokens - 5.5x more expensive. Input token pricing: DeepSeek V3.2 at $0.26/M vs Qwen3.5-122B-A10B at $0.26/M.
Qwen3.5-122B-A10B has a larger context window of 262,144 tokens compared to DeepSeek V3.2's 163,840 tokens. A larger context window means the model can process longer documents and conversations.