GLM-5.3 vs Kimi K3.1 Pro
GLM-5.3 and Kimi K3.1 Pro are within three points on the Models at Work leaderboard, which we treat as a tie (78 vs 75). GLM-5.3 is about 1.7× cheaper per blended million tokens ($2.15 vs $3.68, one figure an Artificial Analysis estimate). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 46 to 32.
| GLM-5.3 · Z.ai | Kimi K3.1 Pro · Moonshot | |
|---|---|---|
| Leaderboard rank | #6 | #10 |
| Score (0–100) | 78 | 75 |
| Tier | Open weights | Open weights |
| Price per 1M tokens | $1.40 in · $4.40 out | $3.68 blended (estimate) |
| Context window | 1M | 1M |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 32 | 46 |
| Arena Text score | 1478 (rank 27) | — |
| Scale SWE-Bench Pro V2 (mini-swe-agent) | 84.30% | — |
| Scale MCP Atlas | 84.20 | — |
| Arena WebDev score | 1622 (rank 22) | — |
| Terminal-Bench 4.0 (vendor) | — | 65.7% |
| Best for | Agentic coding in Claude Code / Pi-style harnesses, Security research and code auditing, Self-hosted frontier-ish reasoning | Self-hosting at scale, Open-weights requirements, Long-context agent work |
Compare source-qualified API rates and a monthly workload example before estimating your bill.
Choose GLM-5.3 if…
- you need agentic coding in claude code / pi-style harnesses
- you need security research and code auditing
- you need self-hosted frontier-ish reasoning
The open model people actually ship coding agents on. Same 744B base as GLM-5.2 with heavy agentic post-training; drops into Claude Code harnesses and holds its own on SWE-Bench Pro V2 and MCP Atlas at a third of frontier prices.
Caveats: License is muddled: AA lists a custom 'GLM-5.3 License' with commercial restrictions while Arena lists MIT. Read it before you self-host. Permissive on offensive-security tasks; that is a feature for red teams and a governance problem for everyone else. Open weights shipped ~2 weeks after the API; HN worried the public weights were safety-tuned differently.
Choose Kimi K3.1 Pro if…
- you need self-hosting at scale
- you need open-weights requirements
- you need long-context agent work
Open weights, 1M context and 46 on the independent index, which puts a self-hostable model within a few points of Gemini 3.8 Flash-class hosted tiers; the catch is a 2.8T-parameter footprint.
Caveats: Weights are under a custom licence with revenue conditions (Moonshot's Hugging Face card), so check it before calling this open source. Price is Artificial Analysis' blended figure, not a vendor list price; Moonshot's sources read gave no API price. A LocalLLaMA poster argues K3.1 is benchmark-tuned; that is one user's claim, not a finding. The only benchmark Moonshot cites (65.7% Terminal-Bench 4.0) is vendor-run and not counted.
What practitioners say
GLM-5.3: One of the biggest HN launches of the year (1,171 points, 584 comments). Practitioners report it 'fits Claude Code as its own, zero issues', that '$5 in tokens' of Claude work costs '$0.50' on GLM via Pi, and that it 'routinely finds bugs missed by Fable and Sol' in audits. The cyber angle was the controversy: users bragged about adapting kernel exploits that 'Claude and Opus outright refused', and others flagged the risk of nerfed public weights.
Kimi K3.1 Pro: Early and split: a Hacker News thread on the open-weights release weighs hardware needs, benchmarks and cost, and a LocalLLaMA poster argues the model is benchmark-tuned, citing an audit PDF. Too early to score; sentiment drops out.
Questions people ask
Which is better, GLM-5.3 or Kimi K3.1 Pro?
GLM-5.3 and Kimi K3.1 Pro are within three points on the Models at Work leaderboard, which we treat as a tie (78 vs 75). GLM-5.3 is about 1.7× cheaper per blended million tokens ($2.15 vs $3.68, one figure an Artificial Analysis estimate). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 46 to 32.
When should I choose GLM-5.3 over Kimi K3.1 Pro?
Choose GLM-5.3 for agentic coding in claude code / pi-style harnesses, security research and code auditing, self-hosted frontier-ish reasoning. The open model people actually ship coding agents on. Same 744B base as GLM-5.2 with heavy agentic post-training; drops into Claude Code harnesses and holds its own on SWE-Bench Pro V2 and MCP Atlas at a third of frontier prices.
When should I choose Kimi K3.1 Pro over GLM-5.3?
Choose Kimi K3.1 Pro for self-hosting at scale, open-weights requirements, long-context agent work. Open weights, 1M context and 46 on the independent index, which puts a self-hostable model within a few points of Gemini 3.8 Flash-class hosted tiers; the catch is a 2.8T-parameter footprint.
Which is cheaper, GLM-5.3 or Kimi K3.1 Pro?
GLM-5.3 costs $1.40 per million input tokens and $4.40 per million output tokens on Z.ai's list price (Z.ai Pricing, read Nov 9, 2026). Moonshot does not publish a simple list price for Kimi K3.1 Pro; Artificial Analysis estimates a blended $3.68 per million tokens (three input to one output).
Scores come from the Models at Work leaderboard: Score is 0 to 100 and computed, not typed: 55% independent evals (Artificial Analysis, Arena, Scale SEAL, Epoch, each scaled against its natural floor and the best score in this table, then averaged), 15% blended price on a fixed log scale ($0.05 per million is 100, $60 is 0), 15% practitioner sentiment from Signals and community threads, 15% operational fit (context window, open weights). A missing component drops out and the rest are reweighted. A model with no independent eval yet is provisional and ranks below every measured one. Within three points is a tie. Vendor-published figures are labelled and never counted. Prices are vendor list prices where published; blended figures assume three input tokens per output token.