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How to evaluate AI models from new providers: Kimi, GLM, Qwen and Muse Spark

Compare Kimi, GLM, Qwen and Muse Spark with the leaders

Data reviewed on Oct 17, 2026 · LLM Stats indexes and official prices

The data, today

The context

Every month brings models from providers that were not on your radar. In October 2026, four of them (Meta's Muse Spark 1.3, Moonshot AI's Kimi K3, Z.ai's GLM-5.3 and Alibaba Cloud's Qwen3.8 Max) entered LLM Stats' top 15 at prices well below the leaders.

A low price is a reason to evaluate, not to adopt. The decision depends on quality on your task, on region and data policy, and on how much the price will change. This page links the catalog data, the price guide for these models and the comparators.

What to decide

  1. 1

    How much cheaper are they?

    With the support example of 100,000 conversations a month, Muse Spark 1.3 costs $357.50, GLM-5.3 $386, Qwen3.8 Max $540 and Kimi K3 $1,050, versus $700 for Claude Sonnet 5.5 and $1,400 for Claude Opus 5.5.

  2. 2

    And the quality?

    Their LLM Stats index (51.0 to 52.4) sits six to ten points below Claude Opus 5.5 (60.7). It is a composite of different tests: evaluation on your own cases is what rules.

  3. 3

    What risks does the price not show?

    The processing region, the data policy, contractual compliance and API stability. Qwen3.8 Max changes price by region ($2 and $6 versus $1.65 and $4.951).

  4. 4

    How do I test without committing?

    Use quotas and caching: Qwen3.8 Max offers 1 million free tokens for 90 days in Singapore, and Kimi K3, GLM-5.3 and Muse Spark publish cache-read rates of $0.30, $0.26 and $0.15 per million.

  5. 5

    How do I compare with GPT, Claude and Gemini?

    With the AI model comparator and the October price guide, which use the same conditions and the same calculated examples.

Common mistakes

  • Deciding only by index or only by price.
  • Sending customer data without reviewing region and data policy.
  • Assuming today's price will stay the same in two months.

Tools and comparators

AI cost calculatorAI models comparator

Guides to go deeper

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No provider pays for its position. Indexes come from LLM Stats; prices from each provider's standard API. How we measure

Reduce AI costsMeasure tokens per request and pay for big models only when neededAI customer supportWork out cost per conversation without dropping below your quality barDocument search and RAGFind the right passage before asking the model for an answerCoding with AIMeasure cost per coding task and review what the agent writesCut cloud costsFind the hidden spend in egress, operations and idle resourcesHandle traffic spikesAbsorb traffic bursts without dropping requests or the databaseShip to productionDeploy from Git with separate environments and clean configurationChoose a databaseCompare Supabase, Neon, Turso, PlanetScale and D1 by how they billLaunch an MVPDeploy and store data at minimum cost and without surprises
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