API cost decision in 10 seconds

Mistral Nemo vs Qwen3 Coder Next

Pick Mistral Nemo for lower cost; pick Qwen3 Coder Next only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Mistral Nemo for lower cost; pick Qwen3 Coder Next only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Mistral Nemo is estimated at $0.04 vs $0.51 for Qwen3 Coder Next, saving $0.47 (93.1% lower).

Cost-first pickMistral Nemo
Context-first pickQwen3 Coder Next
Sample savings$0.4793.1%
10x traffic gap$4.75

Qwen3 Coder Next has more context, but Mistral Nemo saves $0.47 on the standard workload. At 10x that traffic, the same price gap is about $4.75. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Mistral Nemo stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickMistral NemoQwen3 Coder Next
Input-heavy / RAG5M input + 500K outputMistral Nemo$0.12$0.95
Balanced workload1M input + 1M outputMistral Nemo$0.05$0.91
Output-heavy chatbot1M input + 5M outputMistral Nemo$0.17$4.11
Cheaper input Mistral Nemo $0.02 vs $0.11 / 1M

Mistral Nemo is $0.09 cheaper per 1M input tokens (81.8% lower; 5.5x difference).

Cheaper output Mistral Nemo $0.03 vs $0.8 / 1M

Mistral Nemo is $0.77 cheaper per 1M output tokens (96.2% lower; 26.7x difference).

Larger context Qwen3 Coder Next 131.07K vs 262.14K

Qwen3 Coder Next has 131.07K more context (2x larger).

Sample workload Mistral Nemo $0.04 vs $0.51

Mistral Nemo is $0.47 cheaper on the standard workload (93.1% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Mistral Nemo Calculating… Estimated API cost
Qwen3 Coder Next Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Mistral Nemo has the lower input price; Mistral Nemo has the lower output price; Qwen3 Coder Next offers the larger context window. For the 1M input plus 500K output sample, Mistral Nemo is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.04 for Mistral Nemo and $0.51 for Qwen3 Coder Next.

Best Fit

Choose Mistral Nemo when you care most about lower input-token price, and lower output-token price.

Choose Qwen3 Coder Next when you care most about larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, Mistral Nemo is estimated at $0.04 vs $0.51 for Qwen3 Coder Next, saving $0.47 (93.1% lower).
  • Mistral Nemo is $0.47 cheaper on the standard workload (93.1% lower).
  • Mistral Nemo is $0.09 cheaper per 1M input tokens (81.8% lower; 5.5x difference).
  • Mistral Nemo is $0.77 cheaper per 1M output tokens (96.2% lower; 26.7x difference).
  • Qwen3 Coder Next has 131.07K more context (2x larger).
Head-to-Head Specs
FeatureMistral Nemo
(Mistral)
Qwen3 Coder Next
(Qwen)
Input Price
prompt tokens per 1M
$0.02$0.11
Completion Price
per 1M tokens
$0.03$0.8
Sample Workload Cost
1M input + 500K output
$0.04$0.51
Context Window131.07K262.14K
Release Date
Popularity#34#100

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionMistral NemoOn the standard 1M input plus 500K output workload, Mistral Nemo is estimated at $0.04 vs $0.51 for Qwen3 Coder Next, saving $0.47 (93.1% lower).
High-volume input processingMistral NemoLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMistral NemoLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workQwen3 Coder NextA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

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Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

Mistral catalog

Review all tracked Mistral models before deciding whether this matchup is the right shortlist.

Open Mistral models

Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

Open Qwen models
Mistral Nemo

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...

Qwen3 Coder Next

Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...