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ModelCap

Model decision surface

Compare AI models

Start with DeepSeek V3.2 and Qwen3 Coder Next, or choose any two current ranked language models. Compare capability evidence, price, context, provider availability, and weight access without pretending one field decides every use case.

Current public data

DeepSeek V3.2 vs Qwen3 Coder Next

Live dataset updated 9/15/2026, 9:19:37 PM UTC

Open 1200×630 evidence receipt
Factual comparison of DeepSeek V3.2 and Qwen3 Coder Next
Field
ModelCap position#57#122
Index score58.138.9
EvidenceMeasured4 public benchmark observations across 4 boardsMeasured1 public benchmark observation across 1 board
Input / 1M$0.269$0.12
Output / 1M$0.40$0.80
Pricing statusfreshfresh
Context164K262K
Providers154
Weight accessOpen weightsOpen weights

Decision facts

  • DeepSeek V3.2 is #57; Qwen3 Coder Next is #122 on the same current language board.
  • Index scores are 58.1 for DeepSeek V3.2 and 38.9 for Qwen3 Coder Next. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
  • Both positions use Measured evidence.
  • Listed output price per 1M tokens is $0.40 for DeepSeek V3.2 and $0.80 for Qwen3 Coder Next. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $0.74 for DeepSeek V3.2 versus $0.64 for Qwen3 Coder Next. Qwen3 Coder Next costs 13.3% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.
  • Published context is 163,840 tokens for DeepSeek V3.2 and 262,144 for Qwen3 Coder Next.
  • ModelCap currently lists 15 providers for DeepSeek V3.2 and 4 for Qwen3 Coder Next.

These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.

Each comparison page is a permanent, shareable URL with the same live figures as this tool: ModelCap Index position, API pricing, context window, provider count, weight access and every shared benchmark board.