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MiniMax M2.7 vs Qwen3 6 Plus 2026 04 02
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.
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 6 Plus 2026 04 02 has about 1.3× the context window of the other in this pair.
Qwen3 6 Plus 2026 04 02 has 28% more context capacity (262K vs 204K tokens). MiniMax M2.7 is 58% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen3 6 Plus 2026 04 02. Its 262K context fits entire documents without chunking (vs 204K).
RAG / high-volume retrieval
Use MiniMax M2.7. Input tokens are 58% 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 | MiniMax M2.7 | Qwen3 6 Plus 2026 04 02 |
|---|---|---|
| Context window | 204,800 tokens (204K) | 262,144 tokens (262K) |
| Max output tokens | 131,072 tokens (131K) | N/A |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | Yes | No |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Mar 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 | MiniMax M2.7 in | MiniMax M2.7 out | Qwen3 6 Plus 2026 04 02 in | Qwen3 6 Plus 2026 04 02 out |
|---|---|---|---|---|
| Deepinfra | $0.250/M | $1.00/M | — | — |
| Gmi | $0.210/M | $0.840/M | $0.500/M | $3.00/M |
| Novita | $0.300/M | $1.20/M | — | — |
| Openrouter | $0.210/M | $0.840/M | — | — |
| Sambanova | $0.600/M | $2.40/M | — | — |
| Together Ai | $0.300/M | $1.20/M | — | — |
Frequently asked questions
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Use a smaller model.
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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