AI Flash Report

Claude Sonnet 4.6 vs Kimi K2: Benchmarks, Pricing & Capabilities Compared

TL;DR — Claude Sonnet 4.6 wins for coding + reasoning · Kimi K2 wins for cost + long-context.

Claude Sonnet 4.6 Anthropic
Released
2026-02-17
Context window
500K tokens
Input price
$3.00 / Mtok
Output price
$15.00 / Mtok
Key features
  • Agent Teams: orchestrate 2-16 Claude instances
  • Near-Opus performance at 1/5th cost
  • 80.8% SWE-bench Verified
Kimi K2 Moonshot AI
Released
2026-01-20
Context window
2M tokens
Input price
$0.15 / Mtok
Output price
$2.50 / Mtok
Key features
  • First open-weight model #1 on LMSYS Chatbot Arena
  • 1.04 trillion parameters
  • K2.5 agent swarms with up to 100 sub-agents

Benchmark comparison

Benchmark Claude Sonnet 4.6 Kimi K2
GPQA Diamond 78.4% 74.1%
MMLU 92.1% 91.3%
SWE-bench Verified 80.8% 65.8%

Pricing comparison

Metric Claude Sonnet 4.6 Kimi K2
Input ($/Mtok) $3.00 $0.15
Output ($/Mtok) $15.00 $2.50
Cached input ($/Mtok) $0.30
Cost per 1M-token roundtrip (1M in + 1M out) $18.00 $2.65

Context window & modalities

Attribute Claude Sonnet 4.6 Kimi K2
Context window 500K tokens 2M tokens
Input modalities text, image, PDF text, image
Output modalities text text
Knowledge cutoff 2025-10 2025-10

Verdict by use case

Coding
→ Claude Sonnet 4.6
Basis: SWE-bench

Claude Sonnet 4.6 80.8% vs Kimi K2 65.8% on SWE-bench.

Reasoning
→ Claude Sonnet 4.6
Basis: GPQA Diamond

Claude Sonnet 4.6 78.4% vs Kimi K2 74.1% on GPQA Diamond.

Math
Insufficient data
Basis: MATH / AIME

No shared math benchmark.

Long context
→ Kimi K2
Basis: Context window

Claude Sonnet 4.6 500K tokens vs Kimi K2 2M tokens.

Cost
→ Kimi K2
Basis: Input $/Mtok

Claude Sonnet 4.6 $3/Mtok vs Kimi K2 $0.15/Mtok input.

Changelog & releases

Claude Sonnet 4.6
Released 2026-02-17
  • Agent Teams: orchestrate 2–16 Claude instances in parallel
  • +8.5pt on SWE-bench Verified vs Sonnet 4
  • 1/5 the cost of Opus 4.5 at ~95% of coding quality
  • Fast mode research preview for lower-latency inference
Kimi K2
Released 2026-01-20
  • 2M token context window (20x vs first Kimi)
  • Agentic tool-use tuning via MuonClip optimizer
  • Open weights under modified MIT

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