Compare
Qwen3 Coder 30B A3B Instruct vs Sonar Deep Research
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Qwen3 Coder 30B A3B Instruct
Context window
262K
262,144 tokens · ~197K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Qwen3 Coder 30B A3B Instruct has about 2× the context window of the other in this pair.
Qwen3 Coder 30B A3B Instruct has 104% more context capacity (262K vs 128K tokens). Qwen3 Coder 30B A3B Instruct is 96% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen3 Coder 30B A3B Instruct. Its 262K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Qwen3 Coder 30B A3B Instruct. Input tokens are 96% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Qwen3 Coder 30B A3B Instruct | Sonar Deep Research |
|---|---|---|
| Context window | 262,144 tokens (262K) | 128,000 tokens (128K) |
| Max output tokens | 262,144 tokens (262K) | N/A |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | Yes | No |
| Extended thinking | No | Yes |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | Jul 2025 | Mar 2025 |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Qwen3 Coder 30B A3B Instruct in | Qwen3 Coder 30B A3B Instruct out | Sonar Deep Research in | Sonar Deep Research out |
|---|---|---|---|---|
| Fireworks | $0.150/M | $0.600/M | — | — |
| Novita | $0.070/M | $0.270/M | — | — |
| Perplexity | — | — | $2.00/M | $8.00/M |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model