comparison
Kimi K2 Thinking vs Llama 4 Maverick
Token pricing, context window and real monthly cost, side by side. Llama 4 Maverick is the cheaper of the two for a typical workload — about 3.1× less.
Positioned by published specs — size, context and modality — not measured performance; a smaller model can sometimes outperform a larger one on your task.
Llama 4 Maverick and Kimi K2 Thinking are the same class (Flagship) on size & context, so they're plausible substitutes — decide on price, modality and provider fit.
| metric | Kimi K2 Thinking | Llama 4 Maverick |
|---|---|---|
| Input / 1M | $0.60 | $0.20 |
| Output / 1M | $2.50 | $0.80 |
| Context | 262K | 1.0M |
| Technical class | Flagship | Flagship |
| Cost @ typical workload | $430/mo | $140/mo |
| Modality | Text only | text + image |
| Price source | routed | routed |
| Provider | Moonshot | Meta |
Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →
Which should you pick?
On a typical workload, Llama 4 Maverick costs $140/mo against Kimi K2 Thinking's $430/mo — roughly 3.1× 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, Llama 4 Maverick has the larger 1.0M-token window (~1,573 pages). Only Llama 4 Maverick accepts image input — decisive if your prompts include images. By technical class — a spec read of size and context, not measured capability — both are Flagship, so this is a like-for-like price decision.
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 Llama 4 Maverick cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), Llama 4 Maverick costs $140/mo versus $430/mo for Kimi K2 Thinking — about 3.1× 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 Llama 4 Maverick?
On price, Kimi K2 Thinking is $0.60/$2.50 per 1M (in/out) and Llama 4 Maverick is $0.20/$0.80. By technical class (size & context) they're the same — both Flagship. Llama 4 Maverick has the larger context window at 1.0M tokens. Only Llama 4 Maverick accepts image input.
Why is Llama 4 Maverick so much cheaper than Kimi K2 Thinking?
Llama 4 Maverick has a much lower per-token rate — $0.80/1M output versus $2.50. 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.
Which handles longer prompts, Kimi K2 Thinking or Llama 4 Maverick?
Llama 4 Maverick — its 1.0M-token context window (~1,573 pages of text) is the larger of the two, by roughly 4×.
More comparisons
- GPT-5.4 mini vs Kimi K2 Thinking
- GPT-5.4 nano vs Llama 4 Maverick
- Kimi K2 Thinking vs GPT-5.4 nano
- Llama 4 Maverick vs gpt-oss-120b
- Claude Haiku 4.5 vs Kimi K2 Thinking
- Gemini 3.1 Pro vs Kimi K2 Thinking
Related
- Kimi K2 Thinking and Llama 4 Maverick — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.