comparison

MiniMax M2.5 vs gpt-oss-120b

Token pricing, context window and real monthly cost, side by side. gpt-oss-120b is the cheaper of the two for a typical workload — about 4.5× less.

cheaper for a typical workload
gpt-oss-120b
saves 78% vs MiniMax M2.5 at 1,500 in / 500 out × 200,000/mo
MiniMax M2.5 $135/mo
gpt-oss-120b $29.70/mo
MiniMax M2.5 versus gpt-oss-120b specifications and price.
metric MiniMax M2.5 gpt-oss-120b
Input / 1M $0.15 $0.039
Output / 1M $0.90 $0.18
Context 205K 131K
Cost @ typical workload $135/mo $29.70/mo
Modality Text only Text only
Price source routed routed
Provider MiniMax OpenAI

Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →

Which should you pick?

On a typical workload, gpt-oss-120b costs $29.70/mo against MiniMax M2.5's $135/mo — roughly 4.5× cheaper. But the ranking depends on your output-to-input ratio: output is the pricier direction for both, so an output-heavy job (code generation, long answers) widens the gap while an input-heavy one (summarization, retrieval) narrows it. If you need to fit more in a single prompt, MiniMax M2.5 has the larger 205K-token window (~307 pages).

These are list and routed market prices, not measured outcomes. Two models at the same rate can still cost different amounts to finish the same task, because verbose or reasoning-heavy models emit more tokens. That gap is exactly what measured cost-per-task captures.

Frequently asked questions

Is MiniMax M2.5 or gpt-oss-120b cheaper?

For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), gpt-oss-120b costs $29.70/mo versus $135/mo for MiniMax M2.5 — about 4.5× less. Because output is priced higher than input, the winner can flip if your workload writes much more or less than this; check your own numbers in the calculator.

What's the main difference between MiniMax M2.5 and gpt-oss-120b?

On price, MiniMax M2.5 is $0.15/$0.90 per 1M (in/out) and gpt-oss-120b is $0.039/$0.18. MiniMax M2.5 has the larger context window at 205K tokens.

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