minimax-m3 vs kimi-k2
Side-by-side comparison of minimax-m3 and kimi-k2: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. minimax-m3 outperforms kimi-k2 on 6 of 7 shared benchmarks.
MiniMax
minimax-m3
Input / 1M
$0.30
Output / 1M
$1.20
Context
1M
Model ID
minimaxi/minimax-m3
kimi-k2
Input / 1M
$0.60
Output / 1M
$2.50
Context
262K
Model ID
vertex/kimi-k2
Benchmark comparison
Intelligence Indexreasoning
minimax-m354.7%
kimi-k240.9%
Coding Indexcoding
minimax-m343.4%
kimi-k234.8%
Math Indexmath
minimax-m3N/A
kimi-k294.7%
GPQA Diamondreasoning
minimax-m392.9%
kimi-k283.8%
AIME 2025math
minimax-m3N/A
kimi-k294.7%
LiveCodeBenchcoding
minimax-m3N/A
kimi-k285.3%
Terminal-Bench Hardagentic
minimax-m342.4%
kimi-k231.1%
τ²-Benchagentic
minimax-m388.9%
kimi-k293.0%
SciCodecoding
minimax-m345.4%
kimi-k242.4%
MMLU Proknowledge
minimax-m3N/A
kimi-k284.8%
Humanity's Last Examreasoning
minimax-m337.1%
kimi-k222.3%
Scores sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and don't capture every aspect of model quality.
Pricing & specifications
| minimax-m3 | kimi-k2 | |
|---|---|---|
| Input price / 1M | $0.30 | $0.60 |
| Output price / 1M | $1.20 | $2.50 |
| Context window | 1M tokens | 262K tokens |
| Max output | 128K tokens | 262K tokens |
| Vision input | Yes | Yes |
| Tool calling | Yes | Yes |
| Reasoning | Yes | Yes |
| Prompt caching | Yes | Yes |
| Computer use | N/A | N/A |
| Provider | MiniMax | Google LLC (Vertex AI) |
Questions people ask
Is minimax-m3 better than kimi-k2?
minimax-m3 outperforms kimi-k2 on 6 of 7 shared benchmarks. See the benchmark comparison above for specifics: minimax-m3 and kimi-k2 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, minimax-m3 or kimi-k2?
minimax-m3 is cheaper. minimax-m3 costs $0.30/$1.20 per 1M input/output tokens, while kimi-k2 costs $0.60/$2.50.
Can I use minimax-m3 and kimi-k2 through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "minimaxi/minimax-m3" and "vertex/kimi-k2", no other code changes needed.
What are the context windows?
minimax-m3 supports up to 1M tokens of context. kimi-k2 supports up to 262K tokens. Longer context means you can feed larger documents or codebases in a single prompt, though quality often degrades past 128K for most models.
Switch between minimax-m3 and kimi-k2 with one line of code
Requesty provides a single OpenAI-compatible API for 400+ models. Change the model parameter, not your code.
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