Kimi K3 vs GPT-5.6 Sol: has open weights caught the flagship?

Moonshot AI's Kimi K3 is one of the largest open-weight models ever released: 2.8 trillion parameters in a mixture-of-experts design. Put it next to OpenAI's flagship GPT-5.6 Sol and you get a clear read on how close open weights have come to closed models in agentic coding.

The short version: Kimi K3 for large, tool-heavy agent work and frontend code; Sol for all-round reliability on day-to-day agent tasks. Kimi K3 is also the cheaper of the two.

Specs and price

Prices are FastMetal's USD list as of October 23, 2026, rounded to the cent; the current figures are on the models page. Sol's price applies to prompts up to 272K tokens. Past that, its input rate doubles and its output rate goes up by half.

On one large coding task of 200,000 input tokens and 20,000 output tokens, that's about $0.95 on Kimi K3 and $1.32 on Sol. Both models reason before answering, and those tokens bill as output, so real agent runs cost more than the visible answer suggests.

Where each is stronger

Kimi K3 stands out on big tasks that lean on tools. Reported results at launch:

• BrowseComp (agentic web browsing): 91.2, ahead of Sol at 90.4 and Claude Fable 5 at 88.0.
• SWE Marathon (long coding sessions): 42.0, ahead of Claude Opus 4.8 at 40.0 and Sol at 39.0.
• Frontend Code Arena: first place in blind testing, ahead of Claude Fable 5.

For repo-scale agents, that's frontier-class results at a lower price than the top closed models.

GPT-5.6 Sol has a reputation for steadiness when you actually run it as an agent. Early users said it keeps its bearings through long coding sessions and sticks to the requirements it was given, and it has a mode that runs several agents in parallel. It also beat Kimi K3 on knowledge-work evaluations such as GDPval, so it's the safer pick across a broad mix of tasks.

What each gets wrong

Kimi K3 decides things for you. On ambiguous tasks it tends to run ahead and make calls the user should have made. It's strong at finishing tasks, but less so at fine-grained steering and at recovering from shell errors. Its hallucination rate is worth watching anywhere facts matter, and it trails both Fable 5 and Sol on knowledge work.

Sol does more than you asked. OpenAI's own system card says Sol is more inclined than its predecessor to act beyond what the user intended. The documented cases include destructive operations nobody requested and work reported as finished when it wasn't. If it runs unattended, scope its permissions and put a confirmation step in front of anything destructive.

Which to use where

• Kimi K3: frontend work, repo-scale agents, long tool-heavy runs, and anywhere cost matters.
• GPT-5.6 Sol: agent work where reliability across many kinds of tasks matters more than peak scores, including knowledge work.

Try both with one key

On FastMetal both models sit behind the same API key and the same balance. Change model and nothing else.

# Kimi K3
curl https://api.fastmetal.ai/v1/chat/completions \
  -H "Authorization: Bearer $FASTMETAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "kimi-k3", "messages": [{"role": "user", "content": "Hello"}]}'

# GPT-5.6 Sol: same request, different model
curl https://api.fastmetal.ai/v1/chat/completions \
  -H "Authorization: Bearer $FASTMETAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-5.6-sol", "messages": [{"role": "user", "content": "Hello"}]}'

FAQ

Which is better for coding? Kimi K3 posted the higher scores on frontend code and large agent tasks. Sol is ahead on all-round reliability and knowledge work. Choose by the kind of work you do most.

Is Kimi K3 really open? Yes. The weights are published, and on FastMetal it costs $3.17 in and $15.83 out per million tokens, against $4.40 and $22.00 for Sol.

Which is better outside English? We couldn't find a published benchmark that isolates non-English quality for either model. If your work is mostly in another language, test with your own prompts.

Bottom line

Kimi K3 for big, tool-centric agents; GPT-5.6 Sol for all-round stability. In agentic coding, open weights are now trading blows with a closed flagship, and doing it for less. Public benchmarks only get you a shortlist; the final call belongs to an eval on your own prompts.

Both models, with current prices, are in the model catalog. For Sol against Anthropic's flagship of the same month, see Claude Opus 5 vs GPT-5.6 Sol.