Model comparison
GLM 5.2 vs Kimi K3
Z.ai against Moonshot AI, compared on context, price, and verified benchmark results.
Catalog record checked August 14, 2026; individual provider fields may change.
Z.ai
GLM 5.2
Frontier · Open weights
Moonshot AI
Kimi K3
Frontier · Open weights
| Specification | GLM 5.2 | Kimi K3 |
|---|---|---|
| Provider | Z.ai | Moonshot AI |
| Tier | Frontier | Frontier |
| Context window | 1M | Winner: 1.05M |
| Max output | 128K | Winner: 1M |
| Input / 1M tokens | Winner: $1.40 | $3 |
| Output / 1M tokens | Winner: $4.40 | $15 |
| Weights | Open | Open |
| Parameters | Not disclosed | 2.8T total / 104B active (MoE) |
| Reasoning levels | high, max | low, high, max |
| Modalities | text | text, image, video |
| Released | June 16, 2026 | July 16, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-08) | 53 | Winner: 60 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
GLM 5.2
Z.ai's flagship GLM-5.2 coding model with a documented 1M-token context and 128K max output.
Best for
- Open-weight deployments
- Long-horizon coding agents
- Long context on a budget
Watch out
Ecosystem tooling is thinner than the closed frontier labs — budget integration time. Parameter count is not published on the current Z.ai model card, so it stays unverified here. GLM Coding Plan now defaults to GLM 5.3; requests for 5.2/5.1 on that plan are routed to 5.3. This row remains the open-weight / token-list API identity at $1.40/$4.40.
Kimi K3
Open-weight frontier model scoring within a few points of the closed leaders on agentic coding.
Best for
- Agentic coding without vendor lock-in
- Self-hosting at frontier quality
- Long-context work
Watch out
2.8T parameters means self-hosting is a datacentre exercise, not a workstation one — open weights here mean provider choice, not local inference.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
GLM 5.2 vs Kimi K3
Answered from the verified figures on this page rather than general guidance.
Is GLM 5.2 or Kimi K3 cheaper for input?
GLM 5.2 is cheaper at $1.40 per million input tokens, against $3 for Kimi K3 — roughly 2.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate.
Is GLM 5.2 or Kimi K3 cheaper for output?
GLM 5.2 is cheaper at $4.40 per million output tokens, against $15 for Kimi K3 — roughly 3.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate.
Which has the larger context window, GLM 5.2 or Kimi K3?
Kimi K3 accepts 1.05M tokens against 1M for GLM 5.2. This only matters if you routinely send very long documents or large codebases.
Do GLM 5.2 and Kimi K3 support the same reasoning levels?
GLM 5.2 exposes high, max, while Kimi K3 exposes low, high, max.
Should I use GLM 5.2 or Kimi K3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.2 suits open-weight deployments; Kimi K3 suits agentic coding without vendor lock-in.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.