Kimi K3 vs GPT-5.5 and Opus 4.8 (2026): The Cheaper Peer

TL;DR. Artificial Analysis puts Kimi K3’s overall intelligence level with GPT-5.5 and Claude Opus 4.8, and on its GDPval v2 agentic evaluation K3 (1668 Elo) actually scores above both Opus 4.8 (1600) and GPT-5.5 (1494). Only Claude Fable 5 and GPT-5.6 Sol sit clearly above it. K3 does that at a lower price: moonshotai/kimi-k3 runs $3/$15 per million on ofox against $5/$30 for GPT-5.5 and $5/$25 for Opus 4.8, and on AA’s cost-per-task measure K3 comes in around $0.94, roughly half of Opus 4.8’s $1.80. So if you were paying frontier prices for GPT-5.5 or Opus-level work, K3 is the peer that costs less per token, and about half of Opus per finished task. Buy GPT-5.6 Sol or Fable 5 only when you need the top of the board. Everything below is the specs, the benchmarks by source, and the code to A/B all three on one endpoint.

TL;DR: Which One Should You Pick?

One-line verdict: K3 is the value pick against GPT-5.5 and Opus 4.8, matching them on intelligence and beating them on agentic tasks for less money. Pay up only for the very top (GPT-5.6 Sol, Fable 5) or for a specific ecosystem.

The reframe worth internalizing: K3 is not chasing the number-one model. It is landing level with last cycle’s frontier (GPT-5.5) and the current Claude flagship (Opus 4.8) while charging less than either.

Take the first two rows together. For agentic and coding work at the level GPT-5.5 and Opus 4.8 play, K3 is both the strongest on the relevant benchmark and the cheapest. That is the whole article in two lines.

flowchart TD
    A[Task needs frontier-ish quality] --> B{Need the absolute top of the board?}
    B -->|Yes| C[GPT-5.6 Sol or Claude Fable 5]
    B -->|No| D{Locked into the Anthropic ecosystem?}
    D -->|Yes| E[Claude Opus 4.8<br/>anthropic/claude-opus-4.8]
    D -->|No| F{Budget-first, text-only coding?}
    F -->|Yes| G[Kimi K2.7 Code<br/>moonshotai/kimi-k2.7-code]
    F -->|No| H[Kimi K3<br/>moonshotai/kimi-k3<br/>peer intelligence, lower cost]

Quick Specs Comparison

Prices verified on the ofox catalog, July 17 2026, per million tokens. All three flagships take image input; K3’s context window (1M) is its standout.

Read the price rows top to bottom. K3 is the cheapest of the three frontier-class models on every line, input, output, and cache, while Artificial Analysis rates its intelligence level with the other two. K2.7 Code sits in the table as the budget reference: much cheaper, but text-only and a smaller model, so it is a different tier of decision (its own section is further down).

What Kimi K3 Actually Is

Moonshot describes K3 as the first open 3T-class model. The architecture is a 2.8 trillion parameter Mixture-of-Experts they call Stable LatentMoE, activating 16 of 896 experts per token, with an attention stack that pairs Kimi Delta Attention (KDA) with an AttnRes residual scheme. That makes it much larger than the models it competes with on open weights (GLM-5.2 is 753B, DeepSeek V4 Pro 1.6T). The practical headline is simpler: 1M token context, native image input, and thinking baked in.

The thinking behaves like the rest of the current Kimi line, with reasoning exposed in a reasoning_content field. At launch K3 runs at max thinking effort by default, and Moonshot says low- and high-effort modes are coming in subsequent updates, so for now the effort tier is not a dial you can turn down. Reasoning tokens bill as output at $15/M, so a verbose max-thinking run costs more, and today you manage that through prompt and output scope rather than an effort switch. One nice side effect Artificial Analysis measured: K3 used about 21% fewer output tokens than K2.6 to complete their index, so it is more token-efficient than the previous generation even as it scores higher.

On ofox, K3 is moonshotai/kimi-k3 on the OpenAI-compatible endpoint, and the OpenAI protocol path works directly. If you call it through the Anthropic-compatible path instead, the thinking parameter is not forced, which is a behavior change from K2.7. The open weights are not out yet; Moonshot commits to releasing them by July 27 2026, and the license was not stated in the launch post. For now, K3 is an API model.

How K3 Compares to GPT-5.5 and Opus 4.8: Three Reads

Cross-model comparison is where most posts cheat, lining up numbers from different labs run on different harnesses. This section keeps each read to a single source so the comparison is honest.

Read 1: Overall intelligence (Artificial Analysis Index)

The Artificial Analysis Intelligence Index folds many benchmarks into one number so models from different vendors land on the same scale. K3 scores 57. Artificial Analysis calls that intelligence comparable to Claude Opus 4.8 and GPT-5.5, and behind Claude Fable 5 and GPT-5.6 Sol, which lead the board near 60 and 59. It is a rolling snapshot taken around K3’s July 2026 launch, so read it as an ordering with a date, not a fixed score. The short version: K3 has caught up to GPT-5.5 and Opus 4.8 on general intelligence, and has not caught the very top.

Read 2: Agentic tasks (AA GDPval v2)

Overall intelligence understates K3 on the work people actually automate. On Artificial Analysis’s GDPval v2 agentic evaluation, all run on the same harness, K3 ranks above both Opus 4.8 and GPT-5.5, with only Claude Fable 5 and GPT-5.6 Sol higher.

That is a real result, not spin: for agentic tool-use and multi-step work, K3 out-benchmarks the two models this post compares it against. GDPval v2 scores models on realistic, economically valuable tasks rather than trivia, so a lead here maps to the kind of work a coding or ops agent actually does. K3 also takes the top spot on AA’s AutomationBench (their build of Zapier’s agentic SaaS-workflow evaluation) and reaches 1547 on AA-Briefcase, a private long-horizon knowledge-work eval, second only to Fable 5 and up 732 points from Kimi K2.6. Three different agentic tests, same picture: K3 is at or near the top of each, and by far the cheapest of the models scoring up there with it.

Why does an open-ish Kimi lead two closed frontier models on agentic work while sitting level with them on the general index? Because agentic evals reward planning, tool use, and staying on task across many steps, and K3’s default max-thinking budget plus its token efficiency are tuned for exactly that. GPT-5.5 and Opus 4.8 are strong generalists, but neither was built to top an autonomous-agent leaderboard the way this Kimi generation was. If your use case is a single-shot prompt, the general index (where all three are comparable) is the read to trust. If it is an agent that runs for many turns, GDPval is, and K3 wins it.

Read 3: Cost per task (Artificial Analysis)

Per-token prices flatter K3 a little, because a model that reasons more can burn more tokens per job. So the fairer money read is cost per task, which Artificial Analysis measures across its whole index. Here K3 stays cheap even after token usage is counted.

K3 lands around $0.94 per task, about level with GPT-5.5’s $0.99 (from AA’s June v4.1 snapshot) and GPT-5.6 Sol’s $1.04, and roughly half of Opus 4.8’s $1.80. Note the two money reads diverge for the GPT-5.x line: per token K3 is clearly cheaper ($3/$15 vs $5/$30), but per task it lands about even with GPT-5.5, because GPT-5.5 puts fewer tokens through each job. Against Opus 4.8, K3 is cheaper on both reads. The clean takeaway: K3 is materially cheaper than Opus 4.8 for comparable intelligence, cheaper per token than the GPT-5.x flagships, and roughly tied with GPT-5.5 once token usage is counted.

One discipline note before the decision. The three tables above each come from one source, and the Moonshot-reported benchmarks later in this post are a separate system. Do not line up K3’s Moonshot GPQA score against a GPT-5.5 or Opus figure from another harness and call it a head-to-head. The AA reads are the ones where these models sit on a comparable scale.

When to Pick Each One

Against GPT-5.5 and Opus 4.8, the call is capability-per-dollar plus ecosystem:

• Pick Kimi K3 for agentic and coding work at frontier-adjacent quality. It matches GPT-5.5 and Opus 4.8 on overall intelligence, beats them on AA’s agentic eval, and costs less than both, with a 1M context, vision, and open weights on the way. It is the default value choice of the three.
• Pick Claude Opus 4.8 when you are already in the Anthropic ecosystem or want its specific behavior and tooling. Artificial Analysis rates its intelligence level with K3 while its cost per task runs about double, so you are paying for the ecosystem, not a capability gap.
• Pick GPT-5.5 when you want OpenAI’s broad, well-understood generalist and the surrounding tooling. It is a strong all-rounder, but K3 matches its intelligence, beats its agentic Elo, and undercuts its price, so the reason to pick it is familiarity and stack, not raw value.
• Step up to GPT-5.6 Sol or Fable 5 when the task genuinely needs the top of the board. These are the models K3 still trails on the intelligence index. Hard reasoning where the last couple of points change the outcome is the case that justifies the extra cost. Outside coverage frames K3 the same way: The Decoder reads it as nearing the closed leaders rather than passing them.

If K3 is off your table for policy or preference and the choice is just GPT-5.5 versus Opus 4.8, the two are close on the general index but split by character. On AA’s GDPval v2 agentic eval, Opus 4.8 (1600) sits well above GPT-5.5 (1494), so for tool use and multi-step work Opus is the stronger of the pair. On price they match on input ($5/M each) with Opus a little cheaper on output ($25 vs $30/M). GPT-5.5’s advantage is the breadth and maturity of OpenAI’s tooling and the familiarity of its behavior in existing stacks. So between those two: Opus 4.8 for agentic and reasoning-heavy work, GPT-5.5 for a broad, well-supported generalist. The reason K3 leads this comparison is that it matches both on intelligence and beats both on the agentic eval while costing less than either.

Monthly bill: one developer, agentic coding

To make the price gap concrete, here is a per-token monthly bill for a single developer running a reasoning-heavy agent at 20M input and 5M output tokens a month.

K3 runs about 54% of GPT-5.5’s bill and 60% of Opus 4.8’s on the same traffic. Scale that across a five-developer team and K3 saves roughly $575 a month against GPT-5.5 and $450 against Opus 4.8, for work Artificial Analysis rates as comparable in intelligence and stronger on agentic tasks.

The Cheaper Kimi: K2.7 Code

If your work is plain text coding and the frontier is overkill, the relevant comparison is not GPT-5.5 or Opus 4.8, it is a smaller Kimi. Kimi K2.7 Code (moonshotai/kimi-k2.7-code) is a 1T-total, 32B-active, text-only MoE tuned for code, at $0.95/$4 per million with a 256K context and a Modified MIT open-weight license. It is about a third of K3’s price.

The rule is simple. If the job is text-only coding on a budget, K2.7 Code does it for a fraction of the price and the frontier headroom sits idle. If the job needs vision, a 1M context, or the agentic strength in the benchmarks above, K2.7 Code cannot reach it and you are back to K3. For the full token-by-token math on K2.7 Code, including where its 30% thinking-token cut actually lands on a bill, see the K2.7 Code cost breakdown and the K2.7 Code vs GLM-5.2 cost-per-run.

Where K3 Lands on Moonshot’s Own Benchmarks

For completeness, here are the vendor numbers. Every figure is Moonshot-reported, run at max thinking, from the official K3 launch post. Treat them as direction, not independently reproduced, and do not compare them cell-by-cell against the AA reads above, which use different harnesses.

Those track with the third-party picture: strong on science reasoning (GPQA-Diamond) and web-research agents (BrowseComp), consistent with the agentic strength AA measured. Moonshot’s own post is candid that K3 still trails Claude Fable 5 and GPT-5.6 Sol overall, which is why this article positions it as a cheaper peer of GPT-5.5 and Opus 4.8 rather than a new number one.

Run All Three on ofox: A/B in a Few Lines

K3, GPT-5.5, and Opus 4.8 all live on the same OpenAI-compatible endpoint, so testing them against your own task is a one-string swap. Point the SDK at https://api.ofox.ai/v1, loop over the three model IDs, and read usage to compare real token cost. Grab a key on the ofox model page for Kimi K3.

Python: A/B all three in one loop

from openai import OpenAI

client = OpenAI(base_url="https://api.ofox.ai/v1", api_key="YOUR_OFOX_KEY")

task = "Refactor this function to be async and add error handling:\n\n"
task += open("handler.py").read()

for model in ["moonshotai/kimi-k3", "openai/gpt-5.5", "anthropic/claude-opus-4.8"]:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": task}],
    )
    print(f"\n=== {model} ===")
    print(r.usage)   # compare tokens, then multiply by the specs-table prices
    print(r.choices[0].message.content)

Node: same shape

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.ofox.ai/v1",
  apiKey: process.env.OFOX_API_KEY,
});

const task = "Refactor this function to be async and add error handling:\n" + code;

for (const model of ["moonshotai/kimi-k3", "openai/gpt-5.5", "anthropic/claude-opus-4.8"]) {
  const r = await client.chat.completions.create({
    model,
    messages: [{ role: "user", content: task }],
  });
  console.log(`\n=== ${model} ===`);
  console.log(r.usage);
  console.log(r.choices[0].message.content);
}

Attach a screenshot (all three take images)

All three flagships accept image input, so a vision task runs on any of them by swapping the model string. Send the image as an image_url block. The one model this fails on is moonshotai/kimi-k2.7-code, which is text-only.

import base64

with open("layout-bug.png", "rb") as f:
    b64 = base64.b64encode(f.read()).decode()

r = client.chat.completions.create(
    model="moonshotai/kimi-k3",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "This UI screenshot has a layout bug. What is wrong and how do I fix the CSS?"},
            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}},
        ],
    }],
)
print(r.choices[0].message.content)

Run the first loop on a representative slice of your real tasks, sum the usage across a day, and multiply by the specs-table prices. That gives you the actual K3-vs-GPT-5.5-vs-Opus delta for your traffic, which beats any leaderboard for your specific workload. For a wider map of coding models by task, see the real-use coding model ranking and the API pricing comparison.

FAQ

Is Kimi K3 better than GPT-5.5? On Artificial Analysis, K3’s overall intelligence (57) is rated comparable to GPT-5.5, and on the GDPval v2 agentic eval K3 (1668) scores above GPT-5.5 (1494). K3 is also cheaper: $3/$15 vs $5/$30. GPT-5.5 remains a strong generalist, but on value K3 wins for agentic and coding work.

Is Kimi K3 better than Claude Opus 4.8? Artificial Analysis rates their overall intelligence comparable (K3 scores 57, a rolling snapshot), and K3 (1668) edges Opus 4.8 (1600) on GDPval v2. K3 is cheaper, $3/$15 vs $5/$25 per token and about half the cost per task ($0.94 vs $1.80 on AA’s runs). Choose Opus 4.8 for the Anthropic ecosystem, K3 for similar intelligence at roughly half the cost.

How much does Kimi K3 cost on ofox? $3/M input, $15/M output, $0.30/M cache read. That is below both GPT-5.5 and Opus 4.8, which are $5/M input each. The prices match Moonshot’s own API, so there is no ofox markup.

Which is best for agentic coding, K3, GPT-5.5, or Opus 4.8? Among these three, AA’s GDPval v2 agentic eval orders K3 (1668) ahead of Opus 4.8 (1600) and GPT-5.5 (1494). Higher on the full board are Claude Fable 5 and GPT-5.6 Sol. K3 also tops AA’s AutomationBench. For agentic coding among the three, K3 benchmarks best and costs the least.

Does Kimi K3 support image input? Yes, native vision input, text output. GPT-5.5 and Opus 4.8 also take images. Kimi K2.7 Code is text-only, so an image_url call fails on it.

Is Kimi K3 open source? Moonshot announced open weights “by July 27 2026” and calls K3 the first open 3T-class model. As of this writing the weights are not out and the license was not stated, so today you use K3 through a hosted API. Once released it would lead open-weight models on the AA index, ahead of GLM-5.2 and DeepSeek V4 Pro.

How big is Kimi K3? 2.8T total parameters, a Mixture-of-Experts activating 16 of 896 experts per token, with a 1M token context and native vision. Much larger than GLM-5.2 (753B) or DeepSeek V4 Pro (1.6T).

What is the ofox model ID for Kimi K3? moonshotai/kimi-k3 on the OpenAI-compatible endpoint at https://api.ofox.ai/v1. GPT-5.5 is openai/gpt-5.5 and Opus 4.8 is anthropic/claude-opus-4.8, so you A/B all three by swapping one string.

Sources Checked for This Refresh

• Kimi K3 official launch post
• Artificial Analysis: Kimi K3
• ofox model page: Kimi K3
• ofox model page: GPT-5.5
• ofox model page: Claude Opus 4.8
• ofox model page: Kimi K2.7 Code
• The Decoder: Kimi K3 nears GPT-5.6 Sol and Fable 5
• Moonshot API pricing