Kimi K3.1 Pro vs K3.1 Pro Highspeed: Is 2–3× the Speed Worth 2× the Price?

Kimi K3.1 Pro Highspeed is not a smarter model. Moonshot AI describes it as K3.1 Pro "with identical model capabilities", running at "approximately 2–3× the speed". At Moonshot's list price it costs exactly twice as much per input and output token. What you buy is wall-clock time, and nothing else.

The short answer: pay for Highspeed when a person is watching the stream, as in an interactive coding session or a chat feature your users wait on. Stay on Pro for work that runs unattended or spends most of each turn in tools and tests. In AIHubMix's spot check, Highspeed generated about 1.6–1.75× as fast as Pro, not 2–3×. Of each answer's tokens, 85–90% were reasoning, so most of the doubled bill paid for thinking nobody reads. At the measured speeds, each extra dollar bought back about seven and a half minutes of waiting.

Same model, different speed

Moonshot is explicit that nothing but speed changes. The Kimi Code model docs list Highspeed as "the high-speed version of K3.1 Pro, with the same capabilities, faster output, and about twice the quota usage of the regular version". The K3.1 Pro tech blog gives the same 2–3× figure and puts both IDs on the Kimi API.

In Kimi Code, the 1M window depends on the plan tier (see the plans section below). On AIHubMix, both IDs are served on Chat Completions and Messages; Responses returns 404 for them as of 27 October.

Because the models are identical, everything that applies to K3.1 Pro applies to Highspeed: thinking can't be turned off, the default effort is max, and sampling is fixed. On 24 October, AIHubMix returned 400 invalid temperature: only 1 is allowed for this model for temperature=0.2 on both IDs.

Price: double on everything except cache hits

Prices are per million tokens, from Moonshot's USD and CN pricing pages on 24 October. Both IDs are also listed on AIHubMix: see the Kimi K3.1 Pro and K3.1 Pro Highspeed model pages.

Two things stand out. First, cache hits cost the same on both IDs (only cache writes double), so in a long session where most input is cached, the premium is below 2×. That's not a given: on K2.7 Code Highspeed, AIHubMix doubles cache reads too. Second, Highspeed costs more than K3 did. Moonshot lists K3 at $3.00 / $15.00, so moving from K3 to Pro cuts the per-token price by 33%, and moving from K3 to Highspeed raises it by 33%.

How much faster: AIHubMix's spot check

On 24 October, AIHubMix sent one prompt to both models through their main IDs, kimi-k3.1-pro and kimi-k3.1-pro-highspeed: a 600-word explanation of hash-map collisions with one short Python example. Requests were streamed at reasoning_effort: low with an 8,192-token completion cap, three runs per model. Treat this as a spot check on one prompt, not a benchmark.

Output speed is completion tokens divided by the time after the first streamed token. One Pro run hit the 8,192-token cap. An earlier run through AIHubMix's coding channel (coding-kimi-k3.1-pro and coding-kimi-k3.1-pro-highspeed) gave a similar ratio: 153–169 against 95–97 tok/s.

• 1.6–1.75×, not 2–3×. Highspeed's slowest run (138 tok/s) was 1.6× Pro's fastest (88). Its fastest (150) was 1.75× Pro's slowest (85.5). End to end, Highspeed finished in 43–60 seconds against 80–99.
• The wait is mostly thinking. Even at low, both models reasoned 5,100–7,400 tokens before writing an answer of 830–930 tokens. Reasoning made up 85–90% of every completion.
• Highspeed shortens the thinking too. Reasoning is model output, so it streams faster as well. The first answer token arrived after 40–58 seconds instead of 71–91. Both models started streaming reasoning within 2–5 seconds, so if your interface hides reasoning, the first visible word comes about 30 seconds sooner on average.

What one answer costs on each

This is an estimate. It assumes 114 input tokens (the test prompt) and 7,300 completion tokens per answer (the six runs averaged about 7,290), the average measured speeds of 87 and 144 tok/s, and Moonshot's list prices.

• K3.1 Pro: 114 × $2.00/M + 7,300 × $10.00/M = $0.0002 + $0.0730 ≈ $0.073. Generation takes about 7,300 ÷ 87 ≈ 84 seconds.
• K3.1 Pro Highspeed: 114 × $4.00/M + 7,300 × $20.00/M = $0.0005 + $0.1460 ≈ $0.146. Generation takes about 7,300 ÷ 144 ≈ 51 seconds.
• The trade: about $0.073 more per answer to save about 33 seconds. That's roughly $0.13 per minute saved, or about $8 per hour of waiting.

At Moonshot's CN list, the same answer is about ¥0.50 on Pro (114 × ¥13/M + 7,300 × ¥68/M) and about ¥1.00 on Highspeed (114 × ¥26/M + 7,300 × ¥136/M).

The rate doesn't depend on how long the answer is. Almost the whole bill is output, so every extra $10.00 per million output tokens at Moonshot's list buys the same time: a million tokens take about 11,490 seconds at 87 tok/s and 6,940 seconds at 144 tok/s, a saving of about 76 minutes. If the full 2–3× held, the same premium would buy 96–128 minutes, or $0.08–0.10 per minute saved. At the default max effort each answer carries more reasoning, so the seconds and the dollars both grow. The rate per minute stays put as long as the speed ratio holds, which the spot check didn't measure at max.

Whether $8 an hour is cheap depends on who is waiting. A developer blocked on every turn costs far more than that. A nightly batch job costs nothing to wait on.

What Highspeed doesn't speed up

Moonshot's own FAQ answers the question users will ask first, "Why isn't Highspeed noticeably faster?":

Highspeed only speeds up model output. Tool calls (reading/writing files, running commands, etc.) and script execution are unaffected, so when they take up most of a turn the overall speedup feels small.

Here is what that means for an agent turn, as an illustration with stated assumptions. Say the model writes 1,500 tokens (reasoning plus a tool call), then the turn spends 20 seconds running tests. The context is 60,000 tokens with 95% cache hits. Prices are Moonshot's list, cache writes and prompt processing time are left out, and speeds are the spot-check averages.

• Time: 17 s of output + 20 s of tests = 37 s on Pro; 10 s + 20 s = 30 s on Highspeed. That's 1.2× faster.
• Cost: on Pro, 3,000 × $2.00/M + 57,000 × $0.20/M + 1,500 × $10.00/M ≈ $0.006 + $0.011 + $0.015 ≈ $0.032. On Highspeed, $0.012 + $0.011 + $0.030 ≈ $0.053. That's about 1.65× the bill.

In a test-heavy loop, Highspeed costs about 1.65× as much for about 1.2× the speed. The same-priced cache hits soften the bill, but nothing softens the test suite.

The precedent: K2.7 Code Highspeed

Moonshot has sold "same model, faster" before. Its parameter reference says: "kimi-k2.7-code-highspeed is the same model as kimi-k2.7-code with identical parameter constraints; only the output speed differs." AIHubMix prices it at twice the base model, with cache reads doubled as well.

The K2.7 Code Highspeed model page advertises "approximately 180 Tokens/s and up to 260 Tokens/s in short context scenarios". AIHubMix's rolling measurement on the same page showed about 29 tok/s on 24 October, no faster than the base model's routes (about 13–30 tok/s). The page doesn't say how throughput is averaged, and reasoning time, short replies and load can all pull a rolling figure down, so this doesn't prove the variant is slow. It does show why a speed label is worth checking against your own traffic before you pay double for it.

The mistyped-ID trap

A typo in the Highspeed ID doesn't fail loudly in Kimi Code. The docs say "a mistyped model ID falls back to K2.8 Preview without thinking", and the FAQ adds that a wrong Highspeed value "silently falls back to K2.8 Preview without thinking — no speedup". You get an older model, no reasoning, and no speed gain.

The IDs differ by surface, which makes the mistake easy:

• Kimi Code: k3.1-pro-highspeed
• Kimi API and AIHubMix: kimi-k3.1-pro-highspeed
• AIHubMix coding channel: coding-kimi-k3.1-pro-highspeed

Kimi Code's docs name only k3.1-pro-highspeed and kimi-for-coding-highspeed as valid Highspeed IDs there, so check the API-style ID before you paste it into a Kimi Code config. On AIHubMix, a mistyped ID fails loudly instead: kimi-k3.1-pro-highsped returned a 400 on 24 October.

The fastest check uses the fact that K3.1 Pro can't run without thinking: Moonshot says requests with thinking disabled are rejected. A genuine K3.1 Pro response always carries reasoning: reasoning_content in Chat Completions, a thinking block in Messages, and a nonzero reasoning_tokens count in usage. A response with no reasoning means another model answered or thinking was switched off. On AIHubMix, a request that switched thinking off wasn't rejected: it came back 200 with no reasoning on 24 October, which is exactly what this check catches. The script at the end of this post prints that count.

Kimi Code plans: Highspeed spends quota twice as fast

The 1M context window for K3.1 Pro needs Pro and above (Allegretto and above on legacy plans).

On a plan, double quota means half as much work before you hit the limit. For comparison, Kimi Code's older K2.7 Code Highspeed (kimi-for-coding-highspeed) runs at about 6× speed for 3× quota. One more rule matters when you're tempted to toggle: switching models or effort mid-session "invalidates the existing context cache, requiring a new prefill and increasing token usage". Pick Pro or Highspeed when you open a session and keep it.

Pro or Highspeed, by workload

Who needs Highspeed: developers working turn by turn with K3.1 Pro in Kimi Code or an IDE, on a plan with quota to spare, and teams shipping a user-facing feature where the first visible word waits behind thousands of reasoning tokens. Who doesn't: pipelines, background agents, CI and evals, and anyone whose turns are mostly tool time.

An honest caveat for K3 users. The same main-ID run also timed Kimi K3 on the same prompt at low. K3 reasoned about as much (3,013–7,051 tokens) but streamed at 22–26 tok/s and took 154–309 seconds, against 80–99 for K3.1 Pro. If K3 felt slow, plain K3.1 Pro already answered this prompt in less than half the time on average, at a lower per-token price. Highspeed adds another 1.6–1.75× on top, and that last step costs twice as much as Pro. It's one prompt on one day, so treat the K3 gap as a hint rather than a measure of the two models' speed in general, and the run didn't compare answer quality.

Measure it on your own prompt

Both IDs run through AIHubMix with one key; the quick start covers keys and base URLs, and the AIHubMix compare page has both price sheets side by side with a cost calculator. This script streams the same prompt through each ID and prints the timings the spot check used:

import os, time
from openai import OpenAI

client = OpenAI(base_url="https://aihubmix.com/v1", api_key=os.environ["AIHUBMIX_API_KEY"])
PROMPT = ("Write a clear 600-word explanation of how a hash map handles collisions, "
          "with one short code example in Python. Plain prose, no headings.")

for model in ["kimi-k3.1-pro", "kimi-k3.1-pro-highspeed"]:
    start = time.time()
    first_token = first_answer = usage = None
    stream = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": PROMPT}],
        reasoning_effort="low",
        max_completion_tokens=16384,
        stream=True,
        stream_options={"include_usage": True},
    )
    for chunk in stream:
        if chunk.usage:
            usage = chunk.usage
        if not chunk.choices:
            continue
        delta = chunk.choices[0].delta
        now = time.time() - start
        if first_token is None and (getattr(delta, "reasoning_content", None) or delta.content):
            first_token = now
        if first_answer is None and delta.content:
            first_answer = now
    total = time.time() - start
    details = usage.completion_tokens_details
    reasoning = details.reasoning_tokens if details else 0
    speed = usage.completion_tokens / (total - first_token)
    print(f"{model}: first answer {first_answer:.1f}s, total {total:.1f}s, "
          f"{usage.completion_tokens} tokens ({reasoning} reasoning), {speed:.0f} tok/s")
    if not reasoning:
        print("  no reasoning tokens: this response did not come from K3.1 Pro")

The script leaves out temperature, which both IDs reject unless it's 1. It uses Chat Completions; AIHubMix also serves both IDs on Messages, so Claude Code-shaped clients can call them directly. Responses still returns 404 for the main IDs. Run each model several times. One run of a reasoning model is noise, as the spread in the table above shows.

FAQ

Is Kimi K3.1 Pro Highspeed a different model from K3.1 Pro?
No. Moonshot says it has identical model capabilities and only runs faster. On the Kimi API, both IDs share the same parameter constraints.

How much faster is Highspeed?
Moonshot says about 2–3×. In AIHubMix's spot check on one prompt, it streamed at 138–150 tokens per second against 85–88 for Pro, about 1.6–1.75×, and finished in 43–60 seconds against 80–99.

How much more does Highspeed cost?
Twice as much for input and output, the same for cache hits. Moonshot's list is $4.00 / $20.00 per million tokens against $2.00 / $10.00, with cached input at $0.20 on both and cache writes doubled.

Will Highspeed make my coding agent twice as fast?
Only the part where the model is writing. Tool calls, file reads and writes, commands and scripts run at the same speed, so a turn that spends most of its time in tests gets a small gain.

Why isn't Highspeed faster for me in Kimi Code?
Check the model ID first. It must be k3.1-pro-highspeed; a mistyped value silently falls back to K2.8 Preview without thinking. If the ID is right, tool time probably dominates your turns.

Which Kimi Code plans include Highspeed?
New plans from Pro up, and legacy plans from Allegretto up. Highspeed uses about twice the quota of K3.1 Pro.

Can I switch between Pro and Highspeed mid-session?
You can, but Kimi Code warns that switching models invalidates the context cache and forces a new prefill. Choose one when the session starts.

Keep reading: the Kimi K3.1 Pro series

• If you haven't settled whether K3.1 Pro itself is worth the move from K3, this covers what changed and what a real task costs: Kimi K3.1 Pro vs K3: What Changed, What It Costs, and Who Should Switch
• The ID trap is one of several ways a switch goes wrong; this one covers the 400 errors and the max-effort default that drives both the wait and the bill: Migrating to Kimi K3.1 Pro: Fix the 400s, Mind the Max-Effort Default, and Don't Mistype Highspeed
• If you're paying for speed, check how K3.1 Pro compares with its rivals on the same front-end task and on cost per task: Kimi K3.1 Pro vs Qwen 4 vs Claude Fable 5.5: Front-End Quality, Cost per Task, and the Hype Checked

Sources

• Moonshot AI: Kimi Code model configuration
• Moonshot AI: Kimi K3.1 Pro tech blog
• Moonshot AI: Model parameter reference
• AIHubMix: Kimi K3.1 Pro vs Kimi K3.1 Pro Highspeed
• AIHubMix: Kimi K2.7 Code Highspeed model page