Updated
What is Kimi K3.1 Pro Highspeed?
Kimi K3.1 Pro Highspeed is Moonshot AI's faster serving of Kimi K3.1 Pro, released alongside it on October 23, 2026. The weights are identical: the same 2.8-trillion-parameter mixture-of-experts model, post-trained from Kimi K3, with the same 1,048,576 token context and the same benchmark results. What changes is the serving tier, which Moonshot states returns output two to three times faster, at a higher per-token rate. On UnificAlly it runs as moonshotai/kimi-k3.1-pro-highspeed with 131,072 output tokens by default and thinking on every request.
Kimi K3.1 Pro Highspeed key features
Two to three times the output speed
Moonshot states 2-3x the output speed of the standard serving, which Artificial Analysis measures at 73.5 tokens per second. That moves K3.1 Pro from below the median of its class to the fast end without changing a single answer.
Same model, same scores
Moonshot reports 65.7% on Terminal-Bench 4.0, one point behind Claude Opus 5.5 at 66.4%, and 61.2% on Kimi Code Bench 3.0. Artificial Analysis puts the model at 55 on its Intelligence Index, #4 of 215 models as of October 25, 2026.
Cached input at the standard rate
Only input and output tokens cost more on the Highspeed tier. Cache reads bill at the same rate as the standard serving, so a long system prompt or a shared document set reused across turns costs no extra for the speed.
1M token context, 131k default output
The same 1,048,576 token window at one flat rate, with no long-context surcharge. A single response defaults to 131,072 tokens and can be raised to the full 1M. Context caching is automatic, with a 5-minute default TTL and a 1-hour option.
Full agent and tool surface
Tool definitions, tool_choice including required, dynamic tool loading, strict json_schema structured output, and partial mode all run through the same endpoint. Streaming returns reasoning and answer text as separate deltas.
What Kimi K3.1 Pro Highspeed is best for
Interactive coding assistants
Editor and terminal assistants where a person reads the output as it streams.
Chat products
Customer-facing assistants that need frontier answers at conversational latency.
Blocking agent loops
Agents where every step waits on the previous one, so output speed sets the wall clock.
Live research
Search-and-synthesise flows with the same low hallucination rate as K3.1 Pro, returned faster.
Kimi K3.1 Pro Highspeed use cases
Run it behind a coding assistant, where a developer watches the reply stream and every second of latency is felt. Use it for a chat product that needs K3.1 Pro answers at a pace people will wait for. Put it in an agent loop where steps block on each other, so a faster model cuts the total run time rather than just one reply. For unattended batch work, the standard serving does the same job for less.
Kimi K3.1 Pro Highspeed limitations
The speed comes at a higher per-token price for input and output, so it is the wrong pick for batch jobs, overnight runs, and anything nobody is waiting on.
Everything else that limits Kimi K3.1 Pro applies here. Thinking is always on with reasoning_effort defaulting to max, temperature and top_p are fixed upstream, the model is verbose, and the weights do not arrive until November 4, 2026. Moonshot has not published a tokens-per-second figure for the Highspeed tier, only the two-to-three-times claim, and the benchmark table is Moonshot's own, run in its own harness.
Kimi K3.1 Pro Highspeed vs Kimi K3.1 Pro
Identical answers, different wait. Both run the same weights with the same 1M window, the same 131k default output, and the same tool surface, so prompts and outputs transfer with no change. Highspeed returns output two to three times faster and charges more per input and output token, with cache reads at the same rate. Choose Highspeed when a person or a blocking agent is waiting; choose the standard serving when the job runs unattended and cost per token matters more than minutes.
When to use Kimi K3.1 Pro Highspeed
Use Kimi K3.1 Pro Highspeed when the output is read live: chat, interactive coding, and agents where each step blocks the next. Switch to Kimi K3.1 Pro for batch and background work. If you need a fast model and can give up some depth, Claude Sonnet 5.5 and Gemini 3.8 Flash are the usual alternatives.
Kimi K3.1 Pro Highspeed API examples
Call Kimi K3.1 Pro Highspeed by POSTing to /v1/chat/completions, the OpenAI-compatible endpoint shared by every language model in the UnificAlly API. Parameter docs live at docs.unifically.com/models/llm/moonshotai/kimi-k3.1-pro-highspeed.
curl -X POST https://api.unifically.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "moonshotai/kimi-k3.1-pro-highspeed",
"reasoning_effort": "low",
"stream": true,
"messages": [
{ "role": "user", "content": "Explain this stack trace and suggest the one-line fix." }
]
}'
Tokens stream as they generate, with thinking and answer text as separate deltas. Raise reasoning_effort to high or max when the task is hard enough to justify the extra thinking time.
FAQs
People also ask about Kimi K3.1 Pro Highspeed
moonshotai/kimi-k3.1-pro-highspeed, called through the OpenAI-compatible POST /v1/chat/completions endpoint with your UnificAlly API key. The standard serving is moonshotai/kimi-k3.1-pro.
On UnificAlly, $3 per 1M input tokens, $15 per 1M output tokens, and $0.15 per 1M cached input tokens. Cached input costs the same as on the standard serving. Reasoning tokens bill as output. Live rates are on the pricing page.
No. It is the same weights on a faster serving tier. Moonshot states two to three times the output speed of the standard serving, with the same 1M-token context, the same 131k default output, and the same benchmark results.
Moonshot states two to three times the output speed of Kimi K3.1 Pro, which Artificial Analysis measures at 73.5 tokens per second. Moonshot has not published a tokens-per-second figure for the Highspeed serving.
When a person is waiting on the output, such as chat, interactive coding assistants, and agent loops where each step blocks the next. For batch jobs and unattended runs, the standard serving does the same work for less.
No. Thinking runs on every request. reasoning_effort takes low, high, and max, and max is the default, so set low for quick turns where latency matters most.
Yes. Tool definitions, tool_choice including required, dynamic tool loading, strict json_schema structured output, and partial mode all work through the standard chat completions request. Streaming returns reasoning and answer text as separate deltas.
The same as Kimi K3.1 Pro, because it is the same model: Moonshot reports 65.7% on Terminal-Bench 4.0, and Artificial Analysis scores it at 55 on the Intelligence Index, #4 of 215 models as of October 25, 2026.
