GLM 5.2 vs Kimi K2.5: Benchmarks, Pricing, and When to Choose Each

If you're deciding between GLM 5.2 and Kimi K2.5, the short answer is: these are genuinely different models with genuinely different strengths, and the choice comes down to two questions — do you need image or video input, and how much context does your task require?

GLM 5.2 scores 51 on the Artificial Analysis Intelligence Index versus Kimi K2.5's 35, runs three times faster, and has a context window four times larger. Kimi K2.5 is 57% cheaper on input tokens and natively supports text, image, and video input — which GLM 5.2 does not.

Choose GLM 5.2 if your workflow is text-only and you need the highest possible benchmark performance, long-context reasoning over large codebases or documents, or fast API throughput.

Choose Kimi K2.5 if your workflow includes screenshots, diagrams, or video frames, or if you're building a cost-sensitive application where the price gap matters more than the intelligence gap.

Quick Comparison

Benchmark Performance

GLM 5.2 has a substantial lead on the benchmarks that exist across both models. The comparison is complicated by one asymmetry: Kimi K2.5 supports image input, so it gets scored on visual benchmarks where GLM 5.2 has no entry.

The Terminal-Bench gap is the sharpest: 78% vs 46%. That benchmark tests agentic coding and terminal use, which is exactly what GLM 5.2 was designed for. On GPQA Diamond (scientific reasoning), the two models are nearly identical at 89% vs 88%.

The 34% vs 12% gap on APEX-Agents is also significant if you're building long-horizon agentic workflows — GLM 5.2 handles multi-step tasks about three times better on that measure.

Kimi K2.5 scores 75% on MMMU-Pro, the visual reasoning benchmark. GLM 5.2 doesn't participate because it takes no image input. If your application needs visual reasoning, this category belongs entirely to Kimi K2.5.

Pricing Breakdown

Kimi K2.5 is meaningfully cheaper at every price tier.

For a concrete example: a code review task with 30K input tokens and 5K output tokens costs roughly $0.064 with GLM 5.2 and $0.033 with Kimi K2.5 — about 48% less per call. Run that at scale and the savings compound quickly.

The cache hit discount is heavy on both sides (-81% for GLM 5.2, -83% for K2.5), which means repeated-context workflows — large repository analysis, long document processing with the same base prompt — see a significant cost reduction compared to the headline rates. GLM 5.2 is still more expensive in absolute terms even with caching, but the relative gap narrows.

See GLM 5.2's full pricing structure for provider-by-provider comparisons, since rates can vary from the median.

Context Window: 1M vs 256K

This is the clearest single-dimension advantage in the comparison. GLM 5.2 handles 1 million tokens in a single request — roughly 1,500 pages of text. Kimi K2.5 maxes out at 256K tokens, which is still large but about a quarter of GLM 5.2's capacity.

The practical difference shows up in:

• Large codebase analysis. A 500K-token repository fits in one GLM 5.2 call. With K2.5, you're chunking and losing cross-file context.
• Long document processing. Legal contracts, research papers, and production logs can exceed 256K when combined with reasoning instructions.
• Agentic workflows with long histories. Multi-turn agent loops accumulate context. At 256K, you're truncating history that GLM 5.2 can hold in full.

For tasks that stay within 200K tokens — single files, targeted code review, short document Q&A — the context gap is not relevant, and K2.5's lower price may be the deciding factor.

Multimodal vs Text-Only

This is the other dimension where there's no tradeoff, only capability: Kimi K2.5 can process images and video frames; GLM 5.2 cannot. This isn't a limitation of GLM 5.2's quality — it's an architecture decision. As confirmed by Z.ai's official documentation, GLM 5.2 is a text-only model optimized for code and reasoning at scale.

What Kimi K2.5's multimodal capability enables:

• Screenshot-to-code workflows (describe a UI from a screenshot and generate code)
• Diagram and chart understanding
• Video frame analysis for documentation or debugging
• OCR-style document processing (PDFs with embedded images)
• Visual Q&A in multi-turn conversations

If any of these are core to your use case, Kimi K2.5 is the only choice of the two. Workarounds exist — pipe a screenshot through a separate vision model, extract text, then pass to GLM 5.2 — but that adds latency and pipeline complexity.

For workflows that are purely text, GLM 5.2's architecture advantage shows up clearly in the benchmark numbers above.

Speed and Latency

GLM 5.2 is significantly faster.

At 158 tokens per second, GLM 5.2 ranks third among all models measured by Artificial Analysis. Kimi K2.5 at 51 t/s is in the slower third of the same ranking.

For interactive applications — a chat assistant, an IDE copilot, a real-time code reviewer — the speed difference is perceptible. A 500-token response takes roughly 3 seconds from GLM 5.2 and closer to 10 seconds from Kimi K2.5.

For batch processing where you don't care about latency, the speed advantage is less important, though it does affect throughput at scale.

When to Choose GLM 5.2

GLM 5.2 is the better choice when:

• Your task is entirely text or code — no image or video input required
• You're working with large codebases (>200K tokens), long documents, or multi-file analysis
• You need the highest benchmark performance available in the open-weight category
• Response speed matters for interactive or real-time use cases
• You want to self-host the model under an MIT license with no commercial restrictions
• Your workflow involves long-horizon agentic tasks where reliability across many steps matters

GLM 5.2 released in June 2026, five months after Kimi K2.5, and the benchmark gap reflects that recency. It's the current top-scoring open-weight model on the Artificial Analysis Intelligence Index.

When to Choose Kimi K2.5

Kimi K2.5 is the better choice when:

• Your workflow includes screenshots, UI mockups, diagrams, or video frames as input
• You're building a cost-sensitive product and the 57% input price advantage compounds meaningfully at your volume
• Your prompts stay under 200K tokens and the context gap doesn't affect your use case
• You want a reasoning model with visual input support in a single call — no pipeline for vision preprocessing

Kimi K2.5 was released in January 2026, making it one of the earlier entries in the open-weight multimodal reasoning category. Its Modified MIT License is slightly more restrictive than GLM 5.2's MIT, but permits commercial use.

Frequently Asked Questions

Is GLM 5.2 better than Kimi K2.5?

On text benchmarks, yes — GLM 5.2 scores 51 vs 35 on the Artificial Analysis Intelligence Index, and leads on every text benchmark where both models appear. But "better" depends on the task. For visual workflows, Kimi K2.5 is the clear choice since GLM 5.2 doesn't accept image or video input at all.

How much cheaper is Kimi K2.5?

Input tokens are 57% cheaper ($0.60/M vs $1.40/M). Output tokens are 32% cheaper ($3.00/M vs $4.40/M). Cache hit tokens are 62% cheaper ($0.10/M vs $0.26/M). At high call volumes, this adds up significantly.

Can Kimi K2.5 handle a 1M-token codebase?

No. Kimi K2.5's context window is 256K tokens. For repository-scale tasks requiring more than that, GLM 5.2 is the only option of the two.

Does GLM 5.2 support images?

No. GLM 5.2 is a text-only model. For image analysis within the Z.ai ecosystem, the relevant model is GLM-5V-Turbo. For a full explanation, see Is GLM 5.2 Multimodal?

Can I self-host both models?

Yes, both are open-weight models. GLM 5.2 weights are on Hugging Face under the MIT license. Kimi K2.5 weights are available under a Modified MIT License. Both are MoE architectures at scale (~753B and ~1T parameters respectively), so self-hosting requires substantial hardware — see how to run GLM 5.2 locally for setup guidance.

Which model is faster?

GLM 5.2 is about three times faster: 158 t/s vs 51 t/s, with a lower time to first token (1.54s vs 2.85s). For interactive applications, GLM 5.2 is significantly more responsive.

Is Kimi K2.5 the latest Kimi model?

No. Kimi has since released K2.6 and K2.7 Code. K2.5 was released in January 2026. The K2.7 Code model is purpose-built for coding tasks and is architecturally different. If you're specifically comparing GLM 5.2 against the latest Kimi coding model, the comparison target would be K2.7 Code.

Bottom Line

GLM 5.2 is the stronger model on every text benchmark, runs 3x faster, and scales to 1 million tokens. Kimi K2.5 is 57% cheaper on input, supports image and video input, and is sufficient for most tasks that stay within 256K tokens.

For developers building text-intensive, large-context, or agentic applications — particularly on codebases — GLM 5.2 is the better tool. For teams that need visual inputs in the same model call, or for applications where API cost is the constraint, Kimi K2.5 covers that ground effectively.

See how GLM 5.2 stacks up on specific benchmarks, what it costs to run at API scale, or how it compares to Fable 5. To try it without an API key: use GLM 5.2 free at glm5.app.

Sources

• GLM-5.2 (max) — Intelligence, Performance & Price Analysis — Artificial Analysis (Intelligence Index: 51, speed: 158 t/s, price: $1.40/$4.40)
• Kimi K2.5 — Intelligence, Performance & Price Analysis — Artificial Analysis (Intelligence Index: 35, speed: 51 t/s, price: $0.60/$3.00)
• GLM 5.2 vs Kimi K2.7 Code: The Definitive Guide for Coding — regolo.ai (benchmark table, pricing analysis)
• GLM-5.2 — Z.AI Developer Documentation (official modality spec: text-only)
• Kimi K2.5 in 2026: The Ultimate Guide — DEV Community (K2.5 architecture and multimodal capabilities)