Claude Opus 5.5 vs Kimi K3: Pricing, Benchmarks & Which to Pick (2026)

Verdict: Claude Opus 5.5 and Kimi K3 are almost level on raw intelligence, so the choice comes down to how they work. Opus 5.5 is faster, more token-efficient and cheaper per token on HiAPI, which makes it the better default for coding agents and knowledge work. Kimi K3 earns its place when you need video input, strong multimodal grounding or an open-weight model, as long as you budget for its long, max-effort reasoning.

Both models are available on HiAPI behind the same OpenAI-compatible API and the same key. You can switch between them by changing one field, which is the best way to settle the question for your own prompts.

At a Glance

Prices are HiAPI rates as of October 2026. Check the pricing page for current numbers. Both models are listed below their public list prices here: Opus 5.5 at roughly half, Kimi K3 at roughly three-quarters.

Intelligence: A Near-Tie

On the Artificial Analysis Intelligence Index, Opus 5.5 scores 58 and Kimi K3 scores 57.1. That gap is too small to decide on by itself. The two models were not tested on many of the same vendor benchmarks, so the remaining numbers below describe each model rather than a strict head-to-head.

Benchmarks are drawn from different indexes and are mostly vendor-reported or third-party snapshots from September–October 2026. Treat them as directional and test on your own tasks.

What reviewers agree on: Opus 5.5 is strong at agentic coding and knowledge work and writes output that is easy to read, though on very long tasks it occasionally loses focus or is slow to wrap up. Kimi K3 stands out for multimodal grounding and video input, and is consistently described as slow and output-heavy.

The Real Cost: Price per Token vs Price per Task

On paper, the two rate cards are close: Opus 5.5 costs $2.02 / $10.08 per million input / output tokens on HiAPI, Kimi K3 costs $2.32 / $11.60. Opus is a little cheaper on both sides.

Using one example workload (1,000 requests a day, 3,000 input and 800 output tokens each, 30 days, no caching), the HiAPI bill comes to:

• Claude Opus 5.5: about $424 a month
• Kimi K3: about $487 a month

That example assumes both models write the same number of output tokens. In practice they do not. Kimi K3 keeps reasoning on and defaults to max effort, and Artificial Analysis measures it producing roughly twice the output tokens of other models on average. Output tokens are the expensive side of the bill, so a Kimi K3 task usually costs more than the rate card implies, and the gap with Opus widens.

Two practical consequences:

• Measure, do not estimate. Run a sample of real prompts through both models and compare the token usage in the responses before you commit a budget.
• Turn Kimi's effort down when you can. Kimi K3 supports low and high reasoning as well as max. For simple extraction or classification, max is rarely needed.

Key Differences

• Speed. Opus 5.5 streams at around 92 tokens per second versus around 36 for Kimi K3. Combined with Kimi's longer reasoning, time-to-answer for interactive apps is noticeably shorter on Opus.
• Token efficiency. Opus 5.5's adaptive reasoning scales effort to the task. Kimi K3's max-by-default approach spends more tokens on the same request.
• Input types. Kimi K3 accepts text, image and video input and ranks first in BenchLM's multimodal and grounding categories. If video understanding is in your pipeline, it is the natural pick of the two.
• Open weights. Kimi K3 is an open-weight model, which matters if your team values that for research or long-term flexibility. Through HiAPI you call it the same way as any other model.
• Long-task behaviour. Opus 5.5 is strong on multi-step agentic work but can occasionally drift on very long runs; clear task boundaries and checkpoints help.

How to Choose

Pick Claude Opus 5.5 if you:

• Run coding agents, refactors or terminal-heavy automation
• Need fast, readable answers in a user-facing product
• Want predictable cost per task at a lower per-token rate
• Work mainly with text and documents

Pick Kimi K3 if you:

• Need to reason over video, or ground answers in images and other visual input
• Prefer an open-weight model for strategic reasons
• Can tolerate slower responses in exchange for long, thorough reasoning
• Will tune reasoning effort and monitor output tokens

A sensible pattern is to use Opus 5.5 as the default and route multimodal or video jobs to Kimi K3.

Try Both With One Key

Both models use the same endpoint and request shape. Start with one, then change the model value to compare.

# Swap "claude-opus-5-5" for "kimi-k3" to run the same request on Kimi K3
curl https://api.hiapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $HIAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-5-5",
    "messages": [
      {"role": "user", "content": "Summarise the trade-offs between a monolith and microservices for a five-person team."}
    ]
  }'

Three prompts worth running through both models:

1. "Read this 40-page product requirements document and list every requirement that conflicts with another one, with section references."
2. "Here is a failing unit test and the function it covers. Explain the root cause, then propose the smallest fix."
3. "Draft a customer-facing incident summary from these internal notes: what happened, impact, and what we changed."

Compare quality, latency and the usage token counts in each response. That last number is where Kimi K3's extra reasoning shows up.

For setup details, see the Claude Opus 5.5 API quickstart and the Kimi K3 API tutorial, or open the Claude Opus 5.5 and Kimi K3 model pages.

FAQ

Which is better, Claude Opus 5.5 or Kimi K3?

On overall intelligence they are nearly tied (58 vs 57.1 on the Artificial Analysis Intelligence Index). Opus 5.5 is better for most text, coding and agentic work because it is faster and more token-efficient. Kimi K3 is better when your task depends on video input or multimodal grounding.

Which is cheaper, Claude Opus 5.5 or Kimi K3?

Opus 5.5 is cheaper per token on HiAPI ($2.02 / $10.08 vs $2.32 / $11.60 per million input / output tokens) and around $424 vs $487 a month in our example workload. Because Kimi K3 tends to generate about twice as many output tokens, its real cost per task is usually higher still.

Why are these models cheaper on HiAPI than the list price?

HiAPI buys capacity at volume and passes the saving on as a plain per-token rate on ordinary real-time requests. There is no batch queue, no minimum spend and no monthly plan. Your balance is pay-as-you-go and never expires.

Why does Kimi K3 use so many output tokens?

Kimi K3 always reasons, and its default effort level is max. Artificial Analysis measures it producing roughly twice the output tokens of other models on average. You can set its reasoning to low or high for tasks that do not need the deepest thinking.

Do I need to change my code to switch between them?

No. HiAPI exposes both through an OpenAI-compatible /v1/chat/completions endpoint with one API key. Change the model field from claude-opus-5-5 to kimi-k3 and keep everything else the same.

Conclusion

Claude Opus 5.5 and Kimi K3 land in almost the same place on intelligence, so the deciding factors are practical. Opus 5.5 is faster, cheaper per token on HiAPI and spends fewer tokens per task, making it the default for coding agents and knowledge work. Kimi K3 is the specialist: open-weight, video-capable and strong at multimodal grounding, with a reasoning style you should budget for. Run your own prompts through both with one key and let the token counts decide.

Related Reading

• Claude Opus 5.5 vs GPT-6 Astra
• Claude Opus 5.5 vs GPT-5.6 Sol
• Claude Opus 5.5 vs Claude Sonnet 4.6
• GPT-6 Astra vs GPT-5.6 Sol
• Claude Opus 5.5 API Quickstart
• Kimi K3 API Tutorial