Compare
DeepSeek V4 Pro vs Qwen3 235b A22b Thinking 2507 Fp8
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 235b A22b Thinking 2507 Fp8
Context window
262K
262,141 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.
DeepSeek V4 Pro has about 4× the context window of the other in this pair.
DeepSeek V4 Pro has 300% more context capacity (1048K vs 262K tokens). Qwen3 235b A22b Thinking 2507 Fp8 is 73% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use DeepSeek V4 Pro. Its 1048K context fits entire documents without chunking (vs 262K).
RAG / high-volume retrieval
Use Qwen3 235b A22b Thinking 2507 Fp8. Input tokens are 73% 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 | DeepSeek V4 Pro | Qwen3 235b A22b Thinking 2507 Fp8 |
|---|---|---|
| Context window | 1,048,576 tokens (1048K) | 262,141 tokens (262K) |
| Max output tokens | 384,000 tokens (384K) | N/A |
| Speed tier | Balanced | Deep |
| Vision | No | No |
| Function calling | Yes | No |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Apr 2026 | N/A |
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 | DeepSeek V4 Pro in | DeepSeek V4 Pro out | Qwen3 235b A22b Thinking 2507 Fp8 in | Qwen3 235b A22b Thinking 2507 Fp8 out |
|---|---|---|---|---|
| Alibaba Cloud | $2.40/M | $4.80/M | — | — |
| Azure | $1.74/M | $3.48/M | — | — |
| Baseten | $1.74/M | $3.48/M | — | — |
| Deepinfra | $1.30/M | $2.60/M | — | — |
| Deepseek | $1.32/M | $3.96/M | — | — |
| Fireworks | $1.20/M | $1.20/M | — | — |
| Gmi | — | — | $0.350/M | $4.20/M |
| Nebius | $1.75/M | $3.50/M | — | — |
| Novita | $1.60/M | $3.20/M | — | — |
| Openrouter | $0.287/M | $0.575/M | — | — |
| Together Ai | $1.74/M | $3.48/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