Kimi K3.1 Pro vs Qwen 4 vs Claude Fable 5.5: Front-End Quality, Cost per Task, and the Hype Checked

Three launches converged on the same October week. On 22 October two anonymous models, Kibitz and Kalmia, appeared in Arena's Code Arena a few hours apart. Tokenizer tests tied them to Moonshot's Kimi and Alibaba's Qwen. A day later, Kimi posted a teaser of K3.1 writing a Three.js scene, and soon after the model was out: Moonshot's launch post introduced Kimi K3.1 Pro in the Kimi app, Kimi Code and the Kimi API. On Anthropic's side, a claude-fable-5-5 listing surfaced in a cloud catalogue on 13 October, and partners were reported to have early access. Alibaba hasn't released Qwen 4 yet; its tiers were named at Apsara in September. Fable 5.5 has only appeared in leaks.

The short answer has two halves. On Moonshot's own launch numbers, K3.1 Pro beats Claude Fable 5.1 on agentic coding (65.7 to 57.9 on Terminal-Bench 4.0) and trails it on ProgramBench and on hard reasoning without tools. Those figures are self-reported, on harnesses that differ by model, against the Fable before 5.5. The independent record is still the last generation's, and it favours Fable: Fable 5.1 scores 53 to Kimi K3's 44 on Artificial Analysis's current index. But it cost $7.63 per index task to K3's $2.00. Kimi's edges are price, long-context retrieval and, against Qwen, human-judged front-end code. Qwen's edge is token price: Qwen3.8-Max's output costs about a third of K3's and under half of K3.1 Pro's. And Kimi's price edge over Fable widens: AIHubMix lists K3.1 Pro at $2.00 input and $10.50 output per million tokens, against $2.84 and $14.20 for K3. The question for this launch week is whether K3.1 Pro and Qwen 4 can close the gap to Fable 5.5 without giving up the price gap. Until independent tests and the other two models arrive, the evidence below is the best there is.

Why these three

• Qwen 4 is the same-week, same-tier rival. Kibitz and Kalmia were tested side by side in Code Arena's battle mode. K3 and Qwen3.8-Max were the two Chinese trillion-parameter flagships of the summer, both with downloadable weights.
• Fable 5.5 is the model both are chasing. Moonshot measured K3 against Claude Fable 5 at launch, and K3.1 Pro against Fable 5.1. Developers who use Claude ask the same thing about every Kimi release: can it do Fable's job for a fraction of the price?
• One task decides it: a one-prompt 3D front-end build in Three.js. Kimi's teaser is exactly that, and Moonshot lists 3D creation among K3.1 Pro's main gains. Arena testers gave Kalmia prompts like "a 3D cycle riding a cow using three.js". Front end is also the area where Kimi has independent evidence on its side.

What each lab has shown so far

The Kimi column now comes from Moonshot's launch post and API docs. K3.1 Pro is "the first model in our new K3.1 series", a post-trained version of K3 with 3T parameters (K3 has 2.8T) and image and video input. Thinking is always on, effort runs low, high or max and defaults to max, and output can run to 1,048,576 tokens. Moonshot says "the full model weights will be released by Nov 4". The price cell is AIHubMix's list for Kimi K3.1 Pro, per million tokens: $2.00 input, $10.50 output and $0.20 cached, against $2.84, $14.20 and $0.284 for K3, so about 30% less on input and 26% less on output.

The other two columns are still leaks or stage announcements. Qwen 4's four tier names (Max, Plus, Flash, 27B) come from attendee reports of Alibaba's Apsara keynote. Its architecture preview, Qwen3.8-Flash-Next, has open weights: 125B main parameters plus 51B of n-gram embeddings, about 6B active per step.

The Fable 5.5 column is the shakiest. The claude-fable-5-5 entry was added to LiteLLM's price list from an Azure catalogue on 13 October. Its price, context and output fields were copied from Fable 5.1 ($10 / $50, 1M context, 128K output). The entry was removed a day later, when nobody could find a deployment. TestingCatalog reported on 17 October that partners have the model. A test model called claude-onigiri-eap was briefly visible on Artificial Analysis and is the likeliest candidate for it.

The hype, checked

Three notes. The price claim came from a registry listing in September that showed K3's price, likely a placeholder. At launch, AIHubMix listed K3.1 Pro about 30% below K3 on input and 26% below on output. The Minecraft claim came from @ChrisGPT on X: K3.1 placed 13,707 blocks for $0.38, against 5,738 blocks for $5.31 from a GPT-6 model. Nobody has published the prompt or the token counts. And anonymous labels have been wrong before: in August a Code Arena model was taken for K3.1 until a tokenizer fingerprint placed it in the Qwen family. K3.1 Pro has now shipped, but Moonshot hasn't said it was Kibitz, so treat Kibitz, Kalmia and Onigiri as strong guesses until the labs confirm.

The last round: K3, Qwen3.8-Max and Fable 5.1

K3.1 Pro's only numbers so far are Moonshot's own, and the other two haven't shipped, so start with the independent record of the generation they replace.

Prices are per million tokens. Both open-weight models come with custom licences.

Kimi vs Fable. Figures in this comparison are from Artificial Analysis, Intelligence Index v4.3.2, both models at max effort.

Fable 5.1 is clearly ahead on hard reasoning and terminal work. Kimi leads only on long-context retrieval and ties on business automation. The price gap is wider than the list prices suggest: Fable 5.1 also spent more tokens per task, so each task cost almost four times as much.

Kimi vs Qwen. Here the independent figures are older: Artificial Analysis results from August 2026 (Intelligence Index v4.1.1), as compiled by Qubrid. K3 scored 57 and Qwen3.8-Max 56, and K3 was cheaper per task ($0.86 to $1.14) because Qwen spent more tokens. In TrilogyAI's blind-reviewed StackPerf test, each model got an hour to analyse the same 269-file codebase (Qwen ran as the 3.8-Max preview). K3 scored 83 and Qwen 80, and the reviewers called the two "complementary". Don't compare the two index versions with each other: Artificial Analysis rescaled the index between them.

Kimi K3.1 Pro vs Fable 5.1 on Moonshot's numbers

Figures are Moonshot's self-reported launch numbers; harnesses differ by model and nobody has reproduced them yet. Moonshot ran most of K3.1 Pro's coding tests in a Claude Code harness and Fable 5.1 at max effort with fallback. Its table has Fable 5.1, not 5.5, and no Qwen model.

The hallucination row is Moonshot's internal Kimi Hallu Bench (hard, with tools), where lower is better.

On these numbers K3.1 Pro passes Fable 5.1 on the long agentic coding tests and makes far fewer factual errors on Moonshot's own test. It trails badly on ProgramBench, trails on Humanity's Last Exam without tools, and roughly ties on OSWorld computer use. Against its predecessor on the same test version, DeepSWE rose from K3's 67.5 to 75.5.

Read the Terminal-Bench row with care. On its own harness, Artificial Analysis measured K3 at 13% on Terminal-Bench 4.0 against Fable 5.1's 52%. Moonshot's 65.7 comes from a different harness, so it isn't a jump from 13 to 65.7, and it doesn't overturn Fable's independent lead until someone reruns it independently.

The deciding task: who builds the better front end?

What Arena testers saw this week. Arena doesn't publish scores for anonymous models, so the only evidence on the new pair is one-shot battles that testers posted on X. TestingCatalog collected the Kibitz reports on 23 October; the Kalmia rows come from the testers' own posts:

The helicopter test, posted by LuminaBench, is the closest thing to this post's deciding task. Both models built takeoff, landing and exploded-part views from the same prompt. Kibitz added X-ray and wireframe modes, and Kalmia a component index and flight telemetry. The tester's verdict was that both are "very strong". Each report is one prompt and one attempt, so none of it ranks the models. No anonymous Fable 5.5 has been reported on Arena. Its likely codename, Onigiri, showed up only in Artificial Analysis's site code.

Against Qwen, Kimi has the evidence. K3 took first place on Arena's Frontend Code leaderboard at launch, with 1,679 points, ahead of Claude Fable 5 (1,631). It led six of the seven frontend domains, and Fable 5 kept only Gaming. Sources disagree on where Qwen3.8-Max stood on the same board, from unranked to fourth, so it gets no number here.

Against Fable, it's no longer clear. On the LMArena WebDev ratings that AIHubMix shows today, Fable 5.1 scores 1,743 to K3's 1,655. That's a different board from the Frontend Code arena K3 topped in July, so the two results don't make a trend. But on the board AIHubMix tracks, Fable is ahead today. Moonshot's own front-end evidence is its internal Design Bench, an Elo rating: K3.1 Pro scores 957, behind GPT-6 Astra (1,076) and ahead of Claude Opus 5.5 (927), and Fable 5.1 wasn't rated. Where K3.1 Pro lands against Fable 5.5 is the first thing to check once both have independent results.

What the build costs. AIHubMix's own K3 run of a one-prompt game build (single HTML file, one reference image) used 74,994 completion tokens, 73% of them thinking, over about 42 minutes. The input wasn't recorded, so the estimates below assume 5,000 tokens. Prices are AIHubMix's.

• K3: 5,000 × $2.84/M + 74,994 × $14.20/M ≈ $1.08
• Kimi K3.1 Pro, same token counts: 5,000 × $2.00/M + 74,994 × $10.50/M ≈ $0.80
• Qwen3.8-Max, same token counts: ≈ $0.39
• Claude Fable 5.1, same token counts: ≈ $4.18
• Kimi K3.1 Pro, Qwen 4 and Fable 5.5, measured: AIHubMix will run the same prompt on all three once all three are available

Same token counts is an assumption, and real counts move the result both ways. On Artificial Analysis's tasks, Fable 5.1 used more output tokens than K3, and Qwen3.8-Max used more than K3. In one hands-on build by Chase AI, K3 used about six times Fable 5's tokens. K3.1 Pro doesn't think less than K3: in an AIHubMix spot check at low effort, both reasoned several thousand tokens on the same prompt (K3.1 Pro 5,561–7,356, K3 3,013–7,051), though K3.1 Pro finished about twice as fast. Measure the build, don't extrapolate it.

What else differs when you switch between them

• Waiting. Artificial Analysis measured Fable 5.1's first token at 255 seconds, because its reasoning isn't streamed, against 3.6 seconds for K3. Yet Fable finished an average task in 722 seconds against K3's 1,231. Fable feels slow to start and finishes sooner. Plan timeouts and progress indicators for both. Kimi's own answer to waiting is K3.1 Pro Highspeed, which Moonshot says has identical capabilities at 2–3× the speed (it streamed 138–150 tokens a second in an AIHubMix spot check, though tool calls and scripts run no faster; the Highspeed post below weighs that against its price).
• Refusals. Fable runs safety classifiers that can decline a request with a normal 200 response and stop_reason: "refusal". Anthropic's stop-reason docs recommend retrying on another Claude model. In Moonshot's own benchmark runs, Fable 5 fell back on 35% of SWE-Marathon tasks. Those are a rival's numbers, but security-adjacent coding should test this early.
• Output length. Fable 5.1 caps output at 128K tokens. K3 and K3.1 Pro allow up to 1,048,576, which matters for whole-file generation and long reports, though K3.1 Pro's default is 131,072 unless you raise it.
• Where it can run. K3's weights are about 1.56 TB and need a large multi-accelerator cluster, but you can host them, and Moonshot says K3.1 Pro's full weights will follow by 4 November. Qwen 4 27B is the tier shown as open, and its predecessor fits on one 80 GB GPU. Fable runs only on Anthropic's API and the clouds that resell it.

Who should pick which

These calls rest on the independent K3, Qwen3.8-Max and Fable 5.1 record, weighed against Moonshot's own K3.1 Pro numbers. Re-check them once independent tests of K3.1 Pro are out and the other two have launched.

• Hardest agentic and terminal work, where a failed run costs more than the tokens: Claude Fable 5.5, until independent tests back Moonshot's numbers. Moonshot puts K3.1 Pro ahead of Fable 5.1 on Terminal-Bench, but the last independent check had Fable 5.1 far ahead of K3 there.
• Front-end and UI generation at volume: start with Kimi K3.1 Pro, and run the Three.js test against Fable 5.5 before committing. Kimi beat Qwen there, but Fable 5.1 leads on WebDev today, and Moonshot's Design Bench didn't rate Fable.
• High-volume, short tasks: Qwen 4, if it keeps Qwen3.8's pricing.
• Long documents and retrieval across a 1M-token context: Kimi K3.1 Pro, priced flat across the context. K3 led Fable 5.1 on long-context retrieval at a quarter of the cost per task.
• Self-hosting or data residency: Kimi or Qwen, with K3.1 Pro's weights due by 4 November. Fable has no weights.

Run all three through one key

AIHubMix serves Kimi, Qwen and Claude models through the same OpenAI-compatible endpoint, so the deciding task is one loop. kimi-k3.1-pro is live on AIHubMix and matches Moonshot's API ID; the Qwen and Claude IDs in the script are guesses until those models ship. On 24 October AIHubMix returned a 400 for both, so as written the script stops after Kimi. To run the current generation today, swap in qwen3.8-max and claude-fable-5-1, which AIHubMix lists. Stay on Chat Completions, as the script does: AIHubMix's Responses endpoint returned 404 for kimi-k3.1-pro on launch night.

import os
from openai import OpenAI

client = OpenAI(base_url="https://aihubmix.com/v1", api_key=os.environ["AIHUBMIX_API_KEY"])
prompt = ("Build a single-file HTML page with Three.js: a low-poly cow riding a bicycle "
          "around a track, with orbit controls. Return only the HTML.")

for model in ["kimi-k3.1-pro", "qwen4-max", "claude-fable-5-5"]:  # Qwen 4 and Fable 5.5 IDs are guesses, not on AIHubMix yet
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        timeout=3600,
    )
    with open(f"{model}.html", "w") as f:
        f.write(r.choices[0].message.content)
    print(model, r.usage.prompt_tokens, r.usage.completion_tokens)

Leave temperature out. Moonshot's docs fix it at 1.0 for K3.1 Pro, as for K3, and when an AIHubMix test sent 0.2 to kimi-k3.1-pro it came back as 400 invalid temperature: only 1 is allowed for this model. Keep the long timeout too: K3.1 Pro thinks at max effort unless told otherwise. Open the three HTML files side by side, then weigh what you see against the token counts printed. AIHubMix's Kimi K3.1 Pro vs Claude Fable 5.1 comparison page puts the current pair side by side.

FAQ

Are Kimi K3.1 Pro, Qwen 4 and Claude Fable 5.5 released?
Kimi K3.1 Pro is: Moonshot released it on 23–24 October in the Kimi app, Kimi Code and its API. When this was written, Qwen 4 had only been named on stage, and Fable 5.5 had only appeared in leaks and a catalogue listing that was later removed.

Can Kimi K3.1 Pro replace Claude Fable 5.5?
Moonshot's own numbers put K3.1 Pro ahead of Fable 5.1 on several agentic coding tests and behind it on ProgramBench and hard reasoning without tools. Those are self-reported and against the previous Fable, and the last independent check had Fable well ahead. Test your own workload on both.

How much cheaper is Kimi than Fable per task?
On Artificial Analysis's index tasks, K3 cost $2.00 per task and Fable 5.1 cost $7.63, a little over a quarter of the cost. K3.1 Pro's per-task cost hasn't been measured yet, but AIHubMix lists it at $2.00 input and $10.50 output per million tokens, against Fable 5.1's $11.00 and $55.00.

Which one is better at front-end code?
K3 led Arena's Frontend Code board at launch, ahead of Fable 5. On LMArena's WebDev ratings today, Fable 5.1 is ahead of K3. Moonshot's internal Design Bench rates K3.1 Pro below GPT-6 Astra and doesn't rate Fable. Qwen3.8-Max has no undisputed score.

Why does Fable take minutes to answer?
Fable's reasoning isn't streamed, so the first visible token arrives only after it finishes thinking. Artificial Analysis measured about 255 seconds for Fable 5.1.

Will Kimi K3.1 Pro have open weights?
Moonshot says the full model weights will be released by 4 November 2026. Qwen 4 27B was shown as an open tier, and Fable has no downloadable weights.

Can I test all three with one API key?
Yes, once all three are listed. AIHubMix serves Kimi, Qwen and Claude models through the same OpenAI-compatible endpoint, so you only change the model ID. Kimi K3.1 Pro is already listed there.

Keep reading: the Kimi K3.1 Pro series

• If you're already on Kimi and the real choice is whether to move up from K3, start with the upgrade itself: Kimi K3.1 Pro vs K3: What Changed, What It Costs, and Who Should Switch
• If the comparison sends you to K3.1 Pro, these are the errors and billing traps to clear first: Migrating to Kimi K3.1 Pro: Fix the 400s, Mind the Max-Effort Default, and Don't Mistype Highspeed
• If Fable's quicker finish is what tempts you, see whether Kimi's faster variant closes the gap at its price: Kimi K3.1 Pro vs K3.1 Pro Highspeed: Is 2–3× the Speed Worth 2× the Price?

Sources

• Moonshot AI: Kimi K3.1 Pro tech blog
• Moonshot AI: Kimi K3 model card and evaluations
• Artificial Analysis: Claude Fable 5.1 vs Kimi K3
• AIHubMix: Kimi K3 vs Claude Fable 5.1
• TestingCatalog: Claude Fable 5.5 reaches partners