Kimi K3 Pricing: API Costs, Context Window, and Value Compared

If you are evaluating Kimi K3 for a production API workload, the first question is whether its performance premium justifies a 3.4x output price increase over comparable alternatives. This article gives you the exact numbers to make that call.

The Short Version

Kimi K3, released by Moonshot AI, charges $3.00 per million input tokens and $15.00 per million output tokens. Its AA Intelligence Index score of 57 places it 6 points above GLM 5.2's score of 51 — but GLM 5.2 costs $1.40/M input and $4.40/M output, making it 53% cheaper on input and 70% cheaper on output. Both models offer a 1M-token context window. The decisive differentiator beyond price is modality: Kimi K3 accepts image input, while GLM 5.2 is text-only. If your pipeline does not need vision, GLM 5.2 delivers strong frontier performance at a fraction of the cost.

Choose Kimi K3 if you need image understanding, the 6-point intelligence advantage materially affects your outputs, and you can absorb the 3.4x output cost premium.

Choose GLM 5.2 if your workload is text-only, throughput and cost efficiency matter, or you need the fastest inference speed among frontier models — 158 t/s versus Kimi K3's 62 t/s.

---

Quick Comparison

---

Benchmark Performance

The AA Intelligence Index is Artificial Analysis's composite benchmark that aggregates capability evaluations across reasoning, coding, and knowledge tasks. Kimi K3 scores 57 and GLM 5.2 scores 51 — a 6-point gap that is real but narrow at the frontier level.

GLM 5.2 has published results on several standard benchmarks:

GLM 5.2's 89% on GPQA Diamond already places it at the frontier of graduate-level scientific reasoning. Its SWE-bench Pro score of 62.1% and Terminal-Bench v2.1 score of 78% reflect strong real-world software engineering capability. For coding-heavy pipelines, these results make GLM 5.2 competitive with models that cost significantly more.

The 6-point AA Intelligence Index gap in Kimi K3's favor is meaningful, but for most text-generation, summarization, and code tasks, the practical difference in output quality will be subtle. The gap matters most in tasks that require deep multi-step reasoning or complex scientific inference.

---

Pricing Breakdown

The cost difference between these two models is large enough to change architecture decisions at scale.

At scale, these differences compound quickly. Consider a workload generating 100 million output tokens per month:

• Kimi K3: 100M × $15.00/M = $1,500/month
• GLM 5.2: 100M × $4.40/M = $440/month
• Monthly savings with GLM 5.2: $1,060

For input-heavy workloads such as document processing or long-context retrieval, the gap on input tokens is equally significant. Processing 1 billion input tokens:

• Kimi K3: 1B × $3.00/M = $3,000
• GLM 5.2: 1B × $1.40/M = $1,400
• Savings with GLM 5.2: $1,600

Cache hit prices are closer ($0.30 vs $0.26), so prompt-caching-heavy architectures narrow the gap somewhat — but GLM 5.2 still comes out ahead across every pricing tier.

---

Speed and Latency

Speed is where the gap between these models is most operationally visible.

GLM 5.2 generates tokens at 158 tokens per second — third fastest among all frontier models measured by Artificial Analysis. Kimi K3 runs at 62 t/s, which is a substantial step back in throughput.

In practical terms: for a 1,000-token response, Kimi K3 takes roughly 16 seconds of generation time versus GLM 5.2's ~6 seconds. At 2,000 tokens, that is 32 seconds versus 13 seconds. For interactive applications where users wait on streamed responses, this difference is user-perceptible.

Time-to-first-token also favors GLM 5.2 at 1.54 seconds versus Kimi K3's 1.99 seconds. That 450ms advantage improves perceived responsiveness in streaming interfaces and IDE assistants where latency shows up in the user experience. For batch processing workloads where throughput affects job queue clearance times, the 2.5x speed difference still translates to faster turnaround at the same hardware cost.

---

Context Window

Both models support a 1M-token context window (exactly 1,048,576 tokens for GLM 5.2). This puts them in the same tier for long-document tasks and removes context length as a differentiating factor between them.

To put 1M tokens in practical terms:

• Roughly 750,000 words — about three full-length novels in a single context
• A software codebase with ~5,000 files averaging 200 tokens per file
• Approximately 35 full earnings call transcripts loaded simultaneously

For RAG pipelines, legal document review, repository-wide code understanding, and research synthesis, both models handle the same maximum input size. If you are currently hitting context limits, either model solves the problem — the choice between them does not hinge on context capacity.

---

Key Differentiators

Modality Support

The most decisive functional difference is image input. Kimi K3 accepts images alongside text, enabling document parsing with embedded figures, screenshot analysis, visual question answering, and product image classification. GLM 5.2 is text-only and cannot process images.

If your application requires vision, Kimi K3 is the only viable option between the two. If your workload is text-only, this differentiator is irrelevant to your cost-benefit calculation.

Architecture and Parameters

Kimi K3 is a 2.8 trillion parameter model. GLM 5.2 uses a Mixture of Experts (MoE) architecture with 753B total parameters but only 40B active parameters per forward pass — 78 transformer layers, 256 experts per layer, with 8 activated per token using Dense Sparse Attention. The active parameter count drives inference cost more than the total parameter count. GLM 5.2's MoE design achieves high capacity without proportional compute overhead, which explains a significant portion of its speed and pricing advantage.

Open Weights and Self-Hosting

Both models have released weights. GLM 5.2 is available on Hugging Face under the standard MIT license (THUDM/GLM-5.2) — commercially usable, modifiable, and redistributable without restriction. Kimi K3's weights were released on July 27, 2026 under a modified MIT license.

Self-hosting Kimi K3's 2.8T parameter model requires substantially more infrastructure than running GLM 5.2's 40B active parameters per token. If self-hosting is your goal for compliance or cost reasons at very high volume, GLM 5.2's operational footprint is considerably more manageable.

---

When to Choose GLM 5.2

• Your workload is text-only with no image or multimodal requirements
• Throughput matters and you need 2.5x faster token generation (158 t/s vs 62 t/s)
• You are running high-volume production workloads where output cost is a primary constraint
• You need a fully permissive MIT license for self-hosting, modification, or redistribution
• You want lower TTFT (1.54s vs 1.99s) for responsive streaming interfaces
• Coding and reasoning benchmarks matter — SWE-bench Pro 62.1%, GPQA Diamond 89%
• You want API access through OpenRouter (z-ai/glm-5.2) for simplified routing

When to Choose Kimi K3

• Your application requires image input — document images, screenshots, visual QA
• The 6-point AA Intelligence Index advantage (57 vs 51) improves output quality on your specific tasks
• You are running low-to-medium volume workloads where the 3.4x output cost premium is manageable
• Response quality per query matters more than throughput for your use case
• You need access to a 2.8T parameter model without investing in self-hosting infrastructure

---

Frequently Asked Questions

What is Kimi K3's output price per million tokens?

Kimi K3 charges $15.00 per million output tokens and $3.00 per million input tokens. Cache hits are priced at $0.30 per million tokens. This makes Kimi K3 3.4x more expensive than GLM 5.2 on output tokens ($15.00 vs $4.40) and 2.1x more expensive on input tokens ($3.00 vs $1.40).

How does Kimi K3 compare to GLM 5.2 on the AA Intelligence Index?

Kimi K3 scores 57 and GLM 5.2 scores 51 on Artificial Analysis's Intelligence Index — a 6-point gap. Kimi K3 holds a measurable intelligence edge, but GLM 5.2 already reaches frontier-level results on standard benchmarks including 89% on GPQA Diamond and 62.1% on SWE-bench Pro. For most production text tasks, the practical quality delta is narrow.

Can I use Kimi K3 for image understanding?

Yes. Kimi K3 accepts both text and image inputs, making it suitable for vision tasks such as document parsing, screenshot analysis, and visual question answering. GLM 5.2 is text-only. If image processing is a requirement, Kimi K3 is the only option between the two models.

Is Kimi K3 open source and self-hostable?

Kimi K3's weights were released on July 27, 2026 under a modified MIT license. GLM 5.2 is also open weights, available on Hugging Face (THUDM/GLM-5.2) under the standard MIT license. Both support self-hosting, though Kimi K3's 2.8T total parameter count requires considerably more infrastructure than GLM 5.2's 40B active parameters per token forward pass.

How much faster is GLM 5.2 than Kimi K3?

GLM 5.2 generates tokens at 158 t/s versus Kimi K3's 62 t/s — approximately 2.5x faster. GLM 5.2 also achieves a lower TTFT at 1.54 seconds compared to Kimi K3's 1.99 seconds. For latency-sensitive applications or high-throughput batch workloads, this speed advantage is a significant operational factor.

Where can I access GLM 5.2 via API?

GLM 5.2 is available through Z.ai's API with base URL https://api.z.ai/v1 and model ID glm-5.2. It is also accessible through OpenRouter as z-ai/glm-5.2. For no-code access without an API key, both models are available directly on glm5.app.

At what output volume does GLM 5.2 pay for itself compared to Kimi K3?

GLM 5.2 saves $10.60 per million output tokens compared to Kimi K3 ($15.00 vs $4.40). Even at modest volumes — 10 million output tokens per month — GLM 5.2 saves $106/month. At 100M output tokens per month, savings reach $1,060. The value equation is clear: unless Kimi K3's quality improvements produce measurable downstream value above that cost delta, GLM 5.2 delivers stronger ROI for text-only workloads.

---

Bottom Line

Kimi K3 is a capable frontier model with a genuine intelligence advantage and multimodal support, but it costs 3.4x more per output token than GLM 5.2 for a 6-point AA Intelligence Index gain. For text-only workloads where speed and cost efficiency matter, GLM 5.2 is the stronger choice — it is 2.5x faster, 71% cheaper on output, fully open-source under MIT, and performs at the frontier on coding and reasoning benchmarks. If your pipeline requires image understanding or the absolute intelligence ceiling of a 2.8T parameter model, Kimi K3's premium is justified. For everything else, GLM 5.2 delivers equivalent utility at a fraction of the price.

Try GLM 5.2 — no API key needed: glm5.app/chat.

---

Sources

• Artificial Analysis — Intelligence Index and Speed Benchmarks
• GLM 5.2 on Hugging Face (THUDM/GLM-5.2)
• Z.ai API Documentation
• OpenRouter — GLM 5.2 Model Page
• Moonshot AI — Kimi Platform