MiniMax-M2.7 vs Qwen3-235B-A22B

MiniMax-M2.7 (228.7B parameters) and Qwen3-235B-A22B (235.1B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.

Side by side

FactMiniMax-M2.7Qwen3-235B-A22B
Parameters228.7B (Mixture-of-experts: 8 of 256 experts active per token (exact active-parameter count not stated on the model card))235.1B
ArchitectureGrouped-query attention; mixture of 256 experts, 8 active per tokenGrouped-query attention; mixture of 128 experts, 8 active per token
Context length200K tokens (204,800)40,960 tokens
LicenseCustom license–
Published precisionF8_E4M3BF16
VRAM needed, As published256 GB525 GB
VRAM needed, FP8Not a smaller option263 GB
VRAM needed, INT4128 GB131 GB
Cheapest live fit, As publishedRTX 4080 Super × 8 · $3.54/hrRTX PRO 6000 × 6 · $8.25/hr
Cheapest live fit, FP8–RTX PRO 6000 × 3 · $4.13/hr
Cheapest live fit, INT4RTX A5000 × 6 · $1.06/hrRTX A5000 × 6 · $1.06/hr
KV cache per token (16-bit)248 KB188 KB
KV cache at 32k tokens7.75 GB5.88 GB
KV cache at 128k tokens31.0 GB23.5 GB

VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.

Which to pick

  • MiniMax-M2.7 needs less VRAM at its published precision (256 GB against 525 GB), so it fits on a smaller GPU.
  • MiniMax-M2.7 lists the longer context window (204,800 tokens against 40,960).
  • Qwen3-235B-A22B caches less per sequence at 32k tokens (5.9 GB against 7.8 GB), leaving more memory for batching.
  • MiniMax-M2.7 has the cheaper live GPU fit at its published precision ($3.54/hr against $8.25/hr).

These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.

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