Model Comparison

Kimi-K2 Instruct vs MiniMax-M1 (80k)

Compare the listed model data for Kimi-K2 Instruct by Moonshot AI and MiniMax-M1 (80k) by MiniMax. Benchmark and pricing records are not a controlled test and may change.

Data last updated: 2026-10-16. Verify current model details with each provider.

What the listed data says

Kimi-K2 Instruct: 128K tokens; MiniMax-M1 (80k): 1M tokens. MiniMax-M1 (80k) has the larger listed context window. Kimi-K2 Instruct is listed for coding, reasoning, agentic workflows, long-context tasks; MiniMax-M1 (80k) is listed for coding, reasoning, agentic workflows, long-context tasks. Shared catalog benchmark entries: SWE-bench Verified: Kimi-K2 Instruct 65.8%; MiniMax-M1 (80k) 56.4%; LiveCodeBench: Kimi-K2 Instruct 75.2%; MiniMax-M1 (80k) 62.9%; AIME 2025: Kimi-K2 Instruct 78.4%; MiniMax-M1 (80k) 76.0%; GPQA Diamond: Kimi-K2 Instruct 75.3%; MiniMax-M1 (80k) 67.8%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.

Catalog source links

Use these model and provider links to verify current specifications, pricing, and availability.

Computed differences from catalog data

  • The listed context windows are 128K tokens and 1M tokens; the larger is 7.81× the smaller.
  • On shared numeric benchmark entries, SWE-bench Verified: Kimi-K2 Instruct − MiniMax-M1 (80k) = +9.4 percentage points (65.8% vs 56.4%); LiveCodeBench: Kimi-K2 Instruct − MiniMax-M1 (80k) = +12.3 percentage points (75.2% vs 62.9%); AIME 2025: Kimi-K2 Instruct − MiniMax-M1 (80k) = +2.4 percentage points (78.4% vs 76.0%); GPQA Diamond: Kimi-K2 Instruct − MiniMax-M1 (80k) = +7.5 percentage points (75.3% vs 67.8%).
  • The first listed parameter quantities are 1T total (32B active via MoE) and 456B total (45.9B active via MoE); the larger quantity is 2.19× the smaller. Total and active MoE parameters are not directly equivalent.
  • The catalog release dates are July 2025 and May 2025, about 2 months apart at the precision supplied.

Cost example: 1M input and 1M output tokens

A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.

Choose Kimi-K2 Instruct if…

Choose Kimi-K2 Instruct when your task aligns with catalog-listed uses or strengths such as Elite software engineering AI: autonomous code review, bug fixing in production repos, Enterprise agentic systems with multi-tool orchestration, Frontier mathematical reasoning, 65.8% SWE-bench Verified — highest open-model software engineering score ever recorded.

Choose MiniMax-M1 (80k) if…

Choose MiniMax-M1 (80k) when your task aligns with catalog-listed uses or strengths such as Entire-repository code analysis, Multi-book research synthesis, Long-running agentic workflows, 1 million token context — entire software repositories in one session.

Kimi-K2 Instruct

Browse Moonshot AI models

Moonshot AI's flagship — 1T MoE param model with exceptional agentic coding, reasoning, and software engineering.

Parameters: 1T total (32B active via MoE)

Context: 128K tokens

Catalog pricing: Moonshot API: beta. Research weights released. Third-party: emerging

Moonshot AI Coding · Reasoning · Agentic · Long Context Open Source

View Kimi-K2 Instruct model profile

MiniMax-M1 (80k)

Browse MiniMax models

MiniMax M1 reasoning model — linear attention architecture enabling very long context at scale with strong reasoning.

Parameters: 456B total (45.9B active via MoE)

Context: 1M tokens

Catalog pricing: MiniMax API pricing; weights released for research

MiniMax Coding · Reasoning · Agentic · Long Context Open Source

View MiniMax-M1 (80k) model profile

Feature Comparison — Kimi-K2 Instruct vs MiniMax-M1 (80k)

Catalog specifications for Kimi-K2 Instruct and MiniMax-M1 (80k)
Specification Kimi-K2 Instruct MiniMax-M1 (80k)
Provider Moonshot AI MiniMax
Parameters 1T total (32B active via MoE) 456B total (45.9B active via MoE)
Context window 128K tokens 1M tokens
Maximum output Not listed Not listed
Catalog pricing Moonshot API: beta. Research weights released. Third-party: emerging MiniMax API pricing; weights released for research
Listed input/output cost per 1M tokens Separate numeric input/output rates not listed Separate numeric input/output rates not listed
License Modified Apache 2.0 (Kimi K2 Model License) MiniMax Model License
Coding support Listed Listed
Reasoning support Listed Listed
Vision input Not listed Not listed
Multimodal Not listed Not listed
Open weights Yes Yes
Benchmark: SWE-bench Verified 65.8% 56.4%
Benchmark: LiveCodeBench 75.2% 62.9%
Benchmark: AIME 2025 78.4% 76.0%
Benchmark: GPQA Diamond 75.3% 67.8%

Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.

Frequently Asked Questions

What is different between Kimi-K2 Instruct and MiniMax-M1 (80k)?
Kimi-K2 Instruct: 128K tokens; MiniMax-M1 (80k): 1M tokens. MiniMax-M1 (80k) has the larger listed context window. Kimi-K2 Instruct is listed for coding, reasoning, agentic workflows, long-context tasks; MiniMax-M1 (80k) is listed for coding, reasoning, agentic workflows, long-context tasks. Shared catalog benchmark entries: SWE-bench Verified: Kimi-K2 Instruct 65.8%; MiniMax-M1 (80k) 56.4%; LiveCodeBench: Kimi-K2 Instruct 75.2%; MiniMax-M1 (80k) 62.9%; AIME 2025: Kimi-K2 Instruct 78.4%; MiniMax-M1 (80k) 76.0%; GPQA Diamond: Kimi-K2 Instruct 75.3%; MiniMax-M1 (80k) 67.8%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for Kimi-K2 Instruct and MiniMax-M1 (80k)?
SWE-bench Verified: Kimi-K2 Instruct 65.8%; MiniMax-M1 (80k) 56.4%; LiveCodeBench: Kimi-K2 Instruct 75.2%; MiniMax-M1 (80k) 62.9%; AIME 2025: Kimi-K2 Instruct 78.4%; MiniMax-M1 (80k) 76.0%; GPQA Diamond: Kimi-K2 Instruct 75.3%; MiniMax-M1 (80k) 67.8%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
Kimi-K2 Instruct: 128K tokens; MiniMax-M1 (80k): 1M tokens. MiniMax-M1 (80k) has the larger listed context window.
What are the listed costs per million tokens for Kimi-K2 Instruct and MiniMax-M1 (80k)?
Kimi-K2 Instruct: Separate numeric input/output rates not listed (Moonshot API: beta. Research weights released. Third-party: emerging). MiniMax-M1 (80k): Separate numeric input/output rates not listed (MiniMax API pricing; weights released for research). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for Kimi-K2 Instruct and MiniMax-M1 (80k)?
The catalog lists Kimi-K2 Instruct as July 2025 and MiniMax-M1 (80k) as May 2025, about 2 months apart at the precision supplied.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between Kimi-K2 Instruct and MiniMax-M1 (80k)?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.

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