Qwen3.8 2.4T vs Kimi K3.

The two trillion-parameter flagships: Qwen's text heavyweight against Kimi's multimodal giant.

Qwen3.8 2.4T and Kimi K3 are the largest models in the catalog, both aimed at hard reasoning and coding, and both holding a million tokens. The difference is sight: Kimi reads text, pictures and video, while Qwen3.8 2.4T is text-only.

Qwen3.8 2.4T carries 2.4 trillion parameters with 95B active per token and costs $6.00 per million tokens. Kimi K3 is larger at 2.8 trillion parameters and costs $8.50. Both support tools and zero retention, so the decision is price, vision and how each handles your hardest task.

At a glance

Where Qwen3.8 2.4T pulls ahead

• Price: $6.00 versus $8.50 per million tokens, about 29% cheaper.
• A sparse design with 95B active parameters per token, built to keep a huge model usable.
• Text-heavy heavyweight reasoning where image input would go unused.

Where Kimi K3 pulls ahead

• The largest model here at 2.8 trillion parameters, for the hardest coding and long agent runs.
• Multimodal input: pictures and video, which Qwen3.8 2.4T cannot read.
• A better fit when the hardest work also involves screenshots, diagrams or video.

The price difference

Kimi K3 costs $8.50 per million tokens against $6.00 for Qwen3.8 2.4T — about 42% more. On UsingOpen you can chat with both for free inside the daily limit, so the rate is a fair proxy for how expensive each model is to run rather than a bill you pay here. Multiply it by your monthly token volume if you are comparing API access elsewhere.

Which should you choose?

Qwen3.8 2.4T is the text specialist of the pair: slightly cheaper, explicitly sparse, and built for heavyweight reasoning without the multimodal overhead. For long text analysis and hard coding, start here.

Kimi K3 is the answer when the task needs sight or the absolute largest model in the catalog. It costs about 42% more than Qwen, so reserve it for work where images or the extra capacity will actually be used.

Chat with Qwen3.8 2.4T free · Chat with Kimi K3 free

Frequently asked questions

Do Qwen3.8 2.4T and Kimi K3 have the same context window?

Yes — both are listed with a 1M context window, roughly 750,000 words. The practical limit is therefore similar, and the decision comes down to price, modalities and behaviour.

Which of Qwen3.8 2.4T and Kimi K3 can read images?

Kimi K3 accepts image input; Qwen3.8 2.4T is text-only. If a screenshot, diagram or video frame is part of the task, only Kimi K3 can handle it.

Is Kimi K3 or Qwen3.8 2.4T better for coding?

Kimi K3 is the closer fit for code here — the catalog tunes it for hard coding and long agent runs — while Qwen3.8 2.4T is aimed at heavyweight reasoning. For project-scale coding, start with Kimi K3; if the task leans toward the other model's strengths, it may still win.

Which is cheaper, Qwen3.8 2.4T or Kimi K3?

Qwen3.8 2.4T is listed at $6.00 per million tokens against $8.50 for Kimi K3 — about 29% cheaper. Both are free to chat with on UsingOpen inside the daily limit when live; the rates matter if you are comparing what API access would cost.

Can I chat with Qwen3.8 2.4T and Kimi K3 for free?

Yes. Both are selectable on UsingOpen without an account when they are live, and each user gets 10 free prompts per day. A model paused for capacity is unavailable until it returns; the picker shows live status. History stays on your device, and Compare mode can send one prompt to both models side by side.

Which model is bigger, Qwen3.8 2.4T or Kimi K3?

Kimi K3 is larger at 2.8 trillion parameters versus 2.4 trillion for Qwen3.8 2.4T. Parameter count is a rough capacity signal, not a guarantee, and Kimi also adds image and video input. Test both on your hardest prompt before paying the difference.

Do both models fit a million tokens?

Yes. Both are listed with a 1M context window, roughly 750,000 words. Neither model can be described as short-context; the choice is price, vision and answer quality.

Related comparisons

• Kimi K3 vs DeepSeek V4 Pro — Two flagships: Moonshot's multimodal giant against DeepSeek's full-strength V4.
• GLM 5.2 vs GLM 5.3 — Two generations of Z.ai's long-work model, with a price gap and a capacity catch.
• Nemotron 3 Ultra vs GPT-OSS 120B — A 550B orchestrator against OpenAI's small, fast open reasoner.

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