Kimi K3.1 Pro vs GLM-5.3
Kimi K3.1 Pro scores higher on the Models at Work leaderboard (86 vs 81 out of 100). GLM-5.3 is about 1.9× cheaper per blended million tokens ($2.15 vs $4). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 55 to 45.
| Kimi K3.1 Pro · Moonshot | GLM-5.3 · Z.ai | |
|---|---|---|
| Leaderboard rank | #2 | #6 |
| Score (0–100) | 86 | 81 |
| Tier | Open weights | Open weights |
| Price per 1M tokens | $2 in · $10 out | $1.40 in · $4.40 out |
| Context window | 1M | 1M |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 55 | 45 |
| Terminal-Bench 4.0 (vendor) | 65.7% | — |
| 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) |
| Best for | Agentic coding with an open-weight exit, 1M-token long-context work, Moving off Kimi K3 | Agentic coding in Claude Code / Pi-style harnesses, Security research and code auditing, Self-hosted frontier-ish reasoning |
Compare source-qualified API rates and a monthly workload example before estimating your bill.
Choose Kimi K3.1 Pro if…
- you need agentic coding with an open-weight exit
- you need 1m-token long-context work
- you need moving off kimi k3
Fourth on the Artificial Analysis index at 55, one point behind Sonnet 5.5, at the same $2 in / $10 out list price, with the weights still to come.
Caveats: Scores within three points of Sonnet 5.5 (tie), and ranks on evals and price alone because no practitioner sentiment exists yet. Moonshot says the full weights arrive by 4 Nov 2026; until then it is open-weight in intent, hosted-only in practice. Artificial Analysis rates it slow (51 tokens/s) and very verbose: 150M tokens to run its index, which is where the $2.11 per-task cost comes from. Moonshot's Terminal-Bench figure is vendor-run and is not counted. No Arena or Scale result yet, and no practitioner threads in Signals, so sentiment is left out.
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.
What practitioners say
Kimi K3.1 Pro: No practitioner threads recorded in Signals yet; left out of the score rather than guessed.
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.
Questions people ask
Which is better, Kimi K3.1 Pro or GLM-5.3?
Kimi K3.1 Pro scores higher on the Models at Work leaderboard (86 vs 81 out of 100). GLM-5.3 is about 1.9× cheaper per blended million tokens ($2.15 vs $4). On Artificial Analysis' independent Intelligence Index, Kimi K3.1 Pro leads 55 to 45.
When should I choose Kimi K3.1 Pro over GLM-5.3?
Choose Kimi K3.1 Pro for agentic coding with an open-weight exit, 1m-token long-context work, moving off kimi k3. Fourth on the Artificial Analysis index at 55, one point behind Sonnet 5.5, at the same $2 in / $10 out list price, with the weights still to come.
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.
Which is cheaper, Kimi K3.1 Pro or GLM-5.3?
Kimi K3.1 Pro costs $2 per million input tokens and $10 per million output tokens on Moonshot's list price (Kimi API model pricing, read Oct 26, 2026). 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 Oct 26, 2026).
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.