GLM-5.3 wins on price ($1.4 vs $3/1M input). Kimi K3 wins on coding (96 vs 91) and writing quality. For most workflows, GLM-5.3 is the stronger default — same price as glm-5.2, far stronger on agents and security.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GLM-5.3 is the safest overall answer here when you want the strongest default instead of the lowest list price.
Mistral: Mistral Nemo is the lower-cost option to start with when you still need useful output at scale.
Kimi K3 is the better pick when response speed matters more than maximum reasoning depth.
Kimi K3 leads on coding with a score of 96 vs 91 for GLM-5.3.
GLM-5.3 is cheaper at $1.4/1M input tokens vs $3/1M for Kimi K3.
GLM-5.3 is the stronger default for coding tasks.
Choose GLM-5.3 for agentic engineering and security work on open weights. Its coding and reasoning scores are what carry the recommendation here.
Kimi K3 earns its place when your work is mostly frontier-level reasoning and agentic coding, even though it loses the overall count here.
Both models serve different primary workflows — GLM-5.3 for agentic engineering and security work on open weights, Kimi K3 for frontier-level reasoning and agentic coding — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
Moonshot / Premium / Aug 6, 2026
Closest Chinese challenger to the frontier — #4 overall on intelligence.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need fast responses or predictable output costs — always-on thinking burns tokens.
The fastest way to see where the recommendation shifts when your priority changes.
Same price as GLM-5.2, far stronger on agents and security.
Closest Chinese challenger to the frontier — #4 overall on intelligence.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GLM-5.3Z.ai | $1.40/1M | $4.40/1M | $23 | 1M tokens | Balanced | 91 | 79 | 82 |
| Kimi K3Moonshot | $3.00/1M | $15.00/1M | $60 | 1M tokens | Deliberate | 96 | 90 | 93 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
Z.ai's newest flagship, aimed squarely at software engineering, autonomous agents and cybersecurity — and the first open-weights model to beat Claude Mythos 5 on a security benchmark.
Your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling.
The clear upgrade over GLM-5.2 at the same price — take it unless you specifically need a published SWE-bench Verified number to sign off on. The security benchmark lead over Mythos 5 is the genuinely new thing here.
Released August 14, 2026. Z.ai list pricing is $1.40/$4.40, the same rate as GLM-5.2; resellers discount from that list. Also available through the GLM Coding Plan from $18/mo. Reported GPQA Diamond 91.7% and Artificial Analysis Intelligence Index 59.5.
Moonshot's 2.8-trillion-parameter multimodal reasoning flagship with always-on thinking — the largest open-weight model ever released and the closest Chinese challenger to the Western frontier.
You need fast responses or predictable output costs — always-on thinking burns tokens.
The first Chinese model to genuinely crowd the Western frontier — #4 on aggregate intelligence ahead of Opus 4.8. The always-on thinking makes it slow and output-heavy, so cost per task runs above the sticker price. A serious Opus-class alternative if latency isn't critical.
Released July 16, 2026; open weights July 26. Cache-hit input $0.30/1M. Subscriptions: Adagio (free) to Vivace $199/mo; full 1M context only on Allegro ($99) and up. New signups paused July 19 near GPU capacity, reopening in batches.
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GLM-5.3 wins on more of the categories we score — coding, reasoning, budget — so it is the better default of the two. Kimi K3 is the better pick when your work is mostly frontier-level reasoning and agentic coding. Neither is universally "better": GLM-5.3 is aimed at agentic engineering and security work on open weights, Kimi K3 at frontier-level reasoning and agentic coding.
GLM-5.3 is cheaper at $1.4/1M input and $4.4/1M output. Kimi K3 costs $3/1M input and $15/1M output.
Both GLM-5.3 and Kimi K3 have the same 1M context window.
Kimi K3 is better for coding with a score of 96 vs GLM-5.3's 91 (out of 100). Claude Fable 5 is the overall coding leader in this directory at 100/100.
GLM-5.3 is faster with a balanced speed rating (score: 3) vs Kimi K3's deliberate rating (score: 2). Speed matters most for interactive and high-throughput work; for batch jobs the Kimi K3 latency penalty is usually invisible.
No published SWE-bench Verified score, so it is absent from the benchmark most buyers compare on. Priced identically to GLM-5.2 at $1.40/$4.40 — the upgrade is capability, not value. Avoid it if your procurement process requires a SWE-bench Verified figure, or you need the closed-frontier reasoning ceiling. That is the main case for looking at Kimi K3 instead.
Most expensive Chinese-lab model ever ($3/$15) with always-on thinking driving high output-token burn and slow responses. 2.8T size makes self-hosting impractical despite open weights; consumer signups were paused July 19 over GPU capacity. Avoid it if you need fast responses or predictable output costs — always-on thinking burns tokens. Against GLM-5.3 specifically, the gap shows up most on coding (91 vs 96).
Take a moderate workload of 10M input and 2M output tokens a month. GLM-5.3 runs $22.80 (at $1.4/1M in and $4.4/1M out); Kimi K3 runs $60.00 (at $3/1M in and $15/1M out). That is a $37.20/month difference — GLM-5.3 is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GLM-5.3 for agentic engineering and security work on open weights, with Kimi K3 handling frontier-level reasoning and agentic coding. Since GLM-5.3 is both the stronger and the cheaper option here, a split mainly makes sense if Kimi K3 covers a capability you specifically need.