comparison
Kimi K2 Thinking 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 4.1× less.
Positioned by published specs — size, context and modality — not measured performance; a smaller model can sometimes outperform a larger one on your task.
Kimi K2 Thinking is one class larger (Flagship vs Mid). Lean to the cheaper gpt-oss-120b unless your task is demanding enough to need the larger class.
| metric | Kimi K2 Thinking | gpt-oss-120b |
|---|---|---|
| Input / 1M | $0.60 | $0.15 |
| Output / 1M | $2.50 | $0.60 |
| Context | 262K | 131K |
| Technical class | Flagship | Mid |
| Cost @ typical workload | $430/mo | $105/mo |
| Modality | Text only | Text only |
| Price source | routed | routed |
| Provider | Moonshot | 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 $105/mo against Kimi K2 Thinking's $430/mo — roughly 4.1× 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, Kimi K2 Thinking has the larger 262K-token window (~393 pages). By technical class (size and context, not measured capability), Kimi K2 Thinking is a Flagship and gpt-oss-120b a Mid — so the lower price partly reflects a smaller class, not just a discount.
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. The technical-class read above is likewise spec-based — size, context and modality, not measured performance — so a smaller model can still outperform a larger one on your specific task.
Frequently asked questions
Is Kimi K2 Thinking or gpt-oss-120b cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), gpt-oss-120b costs $105/mo versus $430/mo for Kimi K2 Thinking — about 4.1× 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 Kimi K2 Thinking and gpt-oss-120b?
On price, Kimi K2 Thinking is $0.60/$2.50 per 1M (in/out) and gpt-oss-120b is $0.15/$0.60. By technical class (size & context) it's Flagship (Kimi K2 Thinking) versus Mid (gpt-oss-120b). Kimi K2 Thinking has the larger context window at 262K tokens.
Why is gpt-oss-120b so much cheaper than Kimi K2 Thinking?
gpt-oss-120b has a much lower per-token rate — $0.60/1M output versus $2.50, and it's a smaller technical class (Mid vs Flagship). The headline rate isn't the whole story, though: a verbose model can cost more to finish a task than its rate implies — that's what measured cost-per-task captures.
More comparisons
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- GPT-5.4 mini vs gpt-oss-120b
- GPT-5.4 mini vs Kimi K2 Thinking
- GPT-5.4 nano vs gpt-oss-120b
- Kimi K2 Thinking vs GPT-5.4 nano
Related
- Kimi K2 Thinking and gpt-oss-120b — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.