GLM 5.2 vs Kimi K3: Benchmarks, Pricing, and Which One to Use

Kimi K3 launched on July 16, 2026 — a 2.8-trillion-parameter reasoning model from Moonshot AI that scores 57 on the Artificial Analysis Intelligence Index and immediately drew comparisons with GLM 5.2. The short answer: K3 benchmarks higher, but you'll pay 3.4× more per output token and wait 2.5× longer for each response.

GLM 5.2 scores 51 on the same index. Where it pulls ahead: $4.40 versus $15.00 per million output tokens, 158 tokens per second versus 62, and MIT-licensed weights already available on Hugging Face. Kimi K3's open weights arrive on July 27; GLM 5.2's are public today.

Choose GLM 5.2 if your workflow is text-only, you're running at scale where the output cost gap compounds, or you need the fastest response times in this tier.

Choose Kimi K3 if you need image input, want the highest benchmark score in the open-weight category, or can absorb the higher token price for a meaningful intelligence gain.

Quick Comparison

Benchmark Performance

Kimi K3 holds a clear lead on most published benchmarks. The gap varies significantly by task type: K3 pulls furthest ahead on GitHub-style coding evaluations, while GLM 5.2 performs comparably or better on agentic terminal tasks.

The DeepSWE and FrontierSWE gaps are K3's strongest argument: 21 and 14 percentage points respectively. Both are real-world software engineering benchmarks evaluating GitHub issue resolution and frontier coding tasks. If your workload resembles those evaluations, K3's advantage is substantial.

Terminal-Bench v2.1 runs in the other direction: GLM 5.2 scores 78%, which tests agentic coding in a terminal environment — shell commands, file navigation, scripting workflows, repeated tool calls. For CI pipelines and automated shell agents, GLM 5.2 holds the lead.

On GPQA Diamond, a doctoral-level scientific reasoning benchmark, both models land at roughly 88–89%. At the top of the science reasoning scale, the intelligence-index gap compresses significantly.

Kimi K3's overall score of 57 places it in the same tier as Claude Opus 4.8 — an unusual achievement for an open-weight model. On LMArena's Frontend Code Arena it debuted at #1 with an Elo of 1679, a strong signal for UI generation and frontend development tasks specifically.

Pricing Breakdown

The price difference between these models is the largest factor in the comparison, and it widens when you look at output tokens specifically.

The 71% output token discount is the number to pay attention to. Most practical API workflows generate substantially more output tokens than input tokens, which means the effective cost difference is closer to the output rate than the blended average.

Concrete example: a code review task with 30K input tokens and 10K output tokens costs $0.086 with GLM 5.2 and $0.24 with Kimi K3 — about 2.8× more per call. At 10,000 daily calls, the annual difference exceeds $560,000.

Cache hit pricing is nearly identical ($0.26 versus $0.30), so repeated-context workflows don't significantly shift the cost comparison. The baseline output rate remains the dominant factor.

See GLM 5.2's pricing breakdown for provider-by-provider comparisons, since rates vary from the Kimi and Z.ai direct APIs.

Speed and Latency

GLM 5.2 generates responses significantly faster. Kimi K3 has a marginal deficit on time to first token as well.

At 158 tokens per second, GLM 5.2 ranks third on the Artificial Analysis speed leaderboard. Kimi K3 at 62 t/s sits in the lower third of the same ranking.

A 500-token response takes roughly 3 seconds from GLM 5.2 and 8 seconds from Kimi K3. For a chat assistant, IDE copilot, or real-time code reviewer, users notice that difference. For overnight batch jobs, it's irrelevant.

One structural note: Kimi K3 is a reasoning model, which means it generates a thinking trace before its final answer. The 1.99s time to first token partially reflects that extended reasoning starting, and output throughput competes with the reasoning step. Applications that stream the thinking trace to users may experience the latency differently than those that wait for the final answer.

Context Window: 1M Tokens Each

Both models support 1 million tokens — approximately 1,500 pages of text in a single request. This dimension is a draw. Neither model imposes a long-context surcharge; pricing is flat across the full context window.

At this context length, both models can process large codebases, full legal contracts, extended research corpora, and long multi-turn agent histories without chunking. For tasks that stay well under 256K tokens, the context advantage is irrelevant to the choice.

Multimodal Capabilities

Kimi K3 accepts text and image input. GLM 5.2 accepts text only.

This is a binary capability gap, not a quality tradeoff. Kimi K3's image support enables:

• Screenshot-to-code workflows (UI mockup or screenshot → implementation)
• Diagram and chart understanding in technical documentation
• OCR-style processing of documents with embedded images
• Visual Q&A across multi-turn conversations

If any of those workflows are core to your application, Kimi K3 is the only option of the two. GLM 5.2's strengths are irrelevant when the task requires image input.

For text-only workflows — code generation, document analysis, reasoning, writing — GLM 5.2's speed and cost advantages apply without this constraint.

Open Source Status

GLM 5.2 weights are on Hugging Face today under the MIT license. Kimi K3 weights are scheduled for July 27, 2026 — eleven days after the API launched.

For air-gapped deployments, self-hosted inference clusters, or fine-tuning pipelines, GLM 5.2 is immediately available. Kimi K3 becomes an option if the July 27 release lands on schedule and your use case can tolerate that timeline.

Hardware requirements diverge significantly at scale. GLM 5.2 runs 753B total parameters with 40B active at any time — still demanding, but tractable with modern GPU clusters. Kimi K3's 2.8 trillion total parameters require substantially more infrastructure to run at full scale. See how to run GLM 5.2 locally for GLM 5.2 requirements.

When to Choose GLM 5.2

GLM 5.2 is the better choice when:

• Your workflow is entirely text or code — no image or diagram input required
• You're running at scale where the 71% output token cost saving compounds meaningfully across calls
• Response speed matters for users: 158 t/s versus 62 t/s is a perceptible difference in interactive applications
• Your agentic tasks involve terminal-style workflows: shell commands, file navigation, repeated scripting steps
• You need open weights available today, under a full MIT license, with no commercial restrictions
• You're choosing between models in the same tier and GPQA-style scientific reasoning is the primary task

GLM 5.2 scores 6 points below Kimi K3 on the AA Intelligence Index. That gap reflects real benchmark performance differences on coding evaluations — DeepSWE and FrontierSWE specifically. But the cost and speed trade-offs are large enough that for many production workloads, GLM 5.2 delivers more value per dollar even with the benchmark gap.

When to Choose Kimi K3

Kimi K3 is the better choice when:

• Your workflow requires image input — UI screenshots, architecture diagrams, document images, video frames
• You want the highest benchmark score available in the open-weight category: 57 on AA Intelligence Index, 76 on AA Coding Index
• GitHub-style coding tasks dominate your workload: issue resolution, pull request review, frontend code generation — the DeepSWE and FrontierSWE gaps (67.5% vs 46.2% and 81.2% vs 67.3%) reflect these scenarios most directly
• You need frontier-tier reasoning comparable to Claude Opus 4.8 in an open-weight model
• Cost per call is a smaller constraint than raw capability: lower-volume professional tools where the per-call price difference is small relative to the value of the output

Kimi K3 is the most capable open-weight model released as of July 2026. Its 2.8T parameter scale and frontier benchmark positioning make it a meaningful step above the previous Kimi K2.x series.

Frequently Asked Questions

Is Kimi K3 better than GLM 5.2?

On most benchmarks, yes — Kimi K3 scores 57 versus 51 on the Artificial Analysis Intelligence Index, and leads on DeepSWE (67.5% vs 46.2%) and FrontierSWE (81.2% vs 67.3%). GLM 5.2 leads on Terminal-Bench and is 71% cheaper on output tokens. Which is "better" depends entirely on whether benchmark scores or cost-efficiency matter more for your workload.

How much more expensive is Kimi K3?

Input tokens cost $3.00/M versus $1.40/M — 54% more. Output tokens cost $15.00/M versus $4.40/M — 241% more, or 3.4× the GLM 5.2 rate. At production call volumes, the output token gap dominates total cost.

Does Kimi K3 support images?

Yes. Kimi K3 accepts text and image input. GLM 5.2 is text-only. For multimodal workflows, Kimi K3 is the only option of the two. For full details on GLM 5.2's modality support, see Is GLM 5.2 Multimodal?

Is Kimi K3 open source?

Kimi K3's API launched on July 16, 2026. Open weights are scheduled for July 27, 2026, under a Modified MIT License permitting commercial use. GLM 5.2 weights are available today under a full MIT license.

What is Kimi K3's context window?

1 million tokens — identical to GLM 5.2. Both models handle large codebases, extended documents, and long agentic histories at this capacity.

Is GLM 5.2 faster than Kimi K3?

Yes. GLM 5.2 generates 158 tokens per second; Kimi K3 generates 62. GLM 5.2 also has a lower time to first token (1.54s versus 1.99s). For interactive applications, GLM 5.2 responds more than twice as fast.

What makes Kimi K3 significant?

Kimi K3 is a 2.8-trillion-parameter model — the largest open-weight model ever released by parameter count at launch. It achieves a benchmark score comparable to Claude Opus 4.8 as an open-weight model, which had previously required proprietary systems. Its Frontend Code Arena debut at #1 on LMArena (1679 Elo) signals strong real-world preference for UI and frontend code generation tasks.

Bottom Line

Kimi K3 is the stronger model on benchmark tests — Intelligence Index 57 versus 51, and substantial leads on GitHub-style coding evaluations. GLM 5.2 is 2.5× faster, 71% cheaper on output tokens, and available open-source today.

The decision reduces to two questions: do you need image input, and does the benchmark gap justify the price premium?

If image input is required: Kimi K3, no tradeoff. If cost or speed is the constraint: GLM 5.2. If raw benchmark performance is the constraint and token cost is secondary: Kimi K3.

See GLM 5.2 benchmark details, provider-by-provider pricing, or how it compares to Kimi K2.5. Try both — no API key needed: GLM 5.2 · Kimi K3.

Sources

• Kimi K3 — Intelligence, Performance & Price Analysis — Artificial Analysis (Intelligence Index: 57, speed: 62 t/s, pricing)
• Kimi K3 vs GLM-5.2 (max): Model Comparison — Artificial Analysis (side-by-side benchmark data)
• Kimi K3 achieves #3 in the Artificial Analysis Intelligence Index — Artificial Analysis
• China's 2.8-trillion-parameter Kimi K3 — Tom's Hardware (parameter count, LMArena Elo)
• Kimi K3 Is Live: Pricing, Benchmarks, and the Wait for Public Weights — Trilogy AI (pricing structure, open-weight timeline)