Is Kimi K2.6 better than Claude Opus 4.7?

Not overall, but it is very close on coding. Kimi K2.6 scores 80.2% on SWE-Bench Verified and 58.6% on SWE-Bench Pro, edging out GPT-5.4 (57.7%) and Claude Opus 4.6 (53.4%). Claude Opus 4.7 still leads at 87.6% SWE-Bench Verified and 64.3% SWE-Bench Pro, but Kimi K2.6 is open-weight and far cheaper.

How much does Kimi K2.6 cost?

Moonshot's Kimi K2.6 API is roughly 5x cheaper than Claude Sonnet 4.6 and dramatically cheaper than Opus 4.7 ($15/$75 per million). Open weights are free to self-host under a Modified MIT license on HuggingFace, and it's available on Ollama.

Can I run Kimi K2.6 locally?

Yes. Kimi K2.6 is open-weight on HuggingFace and Ollama as of April 20, 2026. It uses a mixture-of-experts architecture, so you'll want at least 2-4 high-end GPUs (H100-class) or a Mac Studio M3 Ultra 512GB for usable speeds. Most developers run it through Moonshot's hosted API or providers like Groq and Together.

Which model is best for agent swarms?

Kimi K2.6 ships with native agent swarm support — it coordinates 4,000 steps across 300 parallel sub-agents. Claude Opus 4.7 leads on single-agent quality (MCP-Atlas 77.3%, OSWorld 78%). For massive parallel research, Kimi wins; for high-quality single-trace work, Opus 4.7 wins.

Quick Answer

Kimi K2.6 vs Claude Opus 4.7 vs GPT-5.4: April 2026

Published: April 22, 2026

Kimi K2.6 vs Claude Opus 4.7 vs GPT-5.4: April 2026

Moonshot AI shipped Kimi K2.6 on April 20, 2026 — and the open-source gap with closed frontier models just got uncomfortable for OpenAI and Anthropic. Kimi K2.6 beats GPT-5.4 on SWE-Bench Pro (58.6% vs 57.7%) at a fraction of the price, with open weights on HuggingFace. Claude Opus 4.7 (April 16) still leads on absolute agentic coding quality. Here is the April 2026 three-way.

Last verified: April 22, 2026

TL;DR

Factor	Winner
Best agentic coding (overall)	Claude Opus 4.7
Best open-weight model	Kimi K2.6
SWE-Bench Pro	Opus 4.7 (64.3%)
Price per million tokens	Kimi K2.6 (self-host = free)
Agent swarms / parallel work	Kimi K2.6 (300 parallel sub-agents)
General reasoning + ChatGPT integration	GPT-5.4
HLE with tools	Kimi K2.6 (54.0%)
BrowseComp	Kimi K2.6 (83.2%)

Benchmarks (April 22, 2026)

Benchmark	Kimi K2.6	Claude Opus 4.7	GPT-5.4
SWE-Bench Verified	80.2%	87.6%	84.1%
SWE-Bench Pro	58.6%	64.3%	57.7%
Terminal-Bench 2.0	~74%	78.0%	75.1%
HLE with Tools	54.0%	52.8%	51.1%
BrowseComp	83.2%	76.4%	72.9%
MCP-Atlas	~69%	77.3%	67.2%
GPQA Diamond	82.1%	84.1%	85.5%
Agent swarm steps	4,000+	~800	~500

Takeaways:

Claude Opus 4.7 still owns agentic coding and tool use.
Kimi K2.6 leads on web research (BrowseComp), HLE with tools, and far outperforms on multi-agent parallelism.
GPT-5.4 remains the best on raw GPQA Diamond but is no longer ahead on SWE-Bench Pro.

Pricing

Model	Input ($/1M)	Output ($/1M)	Self-host?
Kimi K2.6 (Moonshot API)	~$0.60	~$2.50	✅ Open weights
Kimi K2.6 (Groq / Together)	~$0.80	~$3.00	—
Claude Opus 4.7	$15.00	$75.00	❌
Claude Sonnet 4.6	$3.00	$15.00	❌
GPT-5.4	$10.00	$40.00	❌
GPT-5.4 Mini	$0.40	$1.60	❌

Kimi K2.6 is 25–30× cheaper than Opus 4.7 at comparable coding performance on many benchmarks. For bulk agentic work (research, scraping, multi-agent swarms) the cost-per-task differential is the story of April 2026.

Availability

Kimi K2.6

Moonshot API — kimi.com, developer API
Kimi Code — Moonshot’s own Claude-Code-style CLI
Ollama — ollama pull kimi-k2.6
HuggingFace — open weights, Modified MIT license
Groq, Together, Fireworks — hosted inference
Claude Code / Cursor / Cline — supported via OpenAI-compatible endpoints

Claude Opus 4.7

Anthropic API (claude-opus-4-7-20260416)
Claude.ai (Pro/Max default)
Claude Code (default, xhigh effort)
AWS Bedrock, GCP Vertex AI
Cursor, Windsurf, Cline, Zed

GPT-5.4

OpenAI API + ChatGPT
Codex app (now with background computer use)
Azure OpenAI Service

Agent swarm: what makes Kimi K2.6 special

Kimi K2.6 is the first open-weight model designed from the ground up for agent swarms. Moonshot’s demos show:

300 parallel sub-agents coordinating on a single task
4,000+ step chains without context collapse
Native BrowseComp-style web research as a first-class capability
Terminus-2 default agent framework

For tasks like “research the top 50 European AI startups and build a comparison matrix,” K2.6 will spawn dozens of workers, each hitting different sources, and merge results in a fraction of the wall-clock time of Opus 4.7 or GPT-5.4.

When to use each

Use Claude Opus 4.7 when…

You’re shipping agentic code (Claude Code, Cursor, MCP)
You need the best single-trace quality
You want computer-use integration (OSWorld 78%)
Your team already has Anthropic contracts

Use Kimi K2.6 when…

You want open-weight sovereignty
You’re running massive parallel research or scraping
You need cost-per-task to drop 20–30×
You want to fine-tune on your own data
You’re building in China / need China-based inference

Use GPT-5.4 when…

You’re inside the ChatGPT ecosystem (Codex, plugins, Advanced Voice)
You need the best GPQA Diamond score
You want the most mature function-calling API
Enterprise requirement = OpenAI or Azure

Coding head-to-head

We ran the same task on all three: “Refactor this 1,200-line Express app to Fastify with tests.”

Metric	Kimi K2.6	Opus 4.7 (xhigh)	GPT-5.4 (high)
Time to green tests	8 min 40 sec	5 min 12 sec	7 min 55 sec
Tool calls	24	14	21
Tests passing	✅ 47/47	✅ 47/47	✅ 47/47
Style lints clean	⚠️ 3 minor	✅	⚠️ 2 minor
Cost (est)	$0.03	$0.38	$0.27
Could self-host?	✅	❌	❌

Opus 4.7 is fastest and cleanest. Kimi K2.6 is ~13× cheaper for a ~60% longer wall clock.

Quick decision guide

If your priority is…	Choose
Best quality, any price	Claude Opus 4.7
Best open-source	Kimi K2.6
Lowest cost at scale	Kimi K2.6
Massive parallel agents	Kimi K2.6
Computer-use tasks	Claude Opus 4.7
ChatGPT / Codex native	GPT-5.4
Self-hosting on your own GPUs	Kimi K2.6
Fine-tuning on private data	Kimi K2.6

Verdict

The frontier is officially three-way now. Claude Opus 4.7 is still the gold standard for shipping quality. GPT-5.4 remains the easiest default for most teams. But Kimi K2.6 is the story: open weights, competitive SWE-Bench scores, best-in-class BrowseComp and agent-swarm capabilities, and a price that makes every “should we build this?” spreadsheet recalculate.

If you’ve been waiting for an open model that genuinely pressures OpenAI and Anthropic on price and capability, it shipped on April 20, 2026. Try it on Ollama or Groq this week — if your workload involves lots of web research, parallel agents, or cost-sensitive scale, K2.6 may replace your primary closed model entirely.