GLM-5.3 vs MiMo-V2.6-Pro
GLM-5.3 and MiMo-V2.6-Pro are within three points on the Models at Work leaderboard, which we treat as a tie (81 vs 81). MiMo-V2.6-Pro is about 4.0× cheaper per blended million tokens ($0.54 vs $2.15). On Artificial Analysis' independent Intelligence Index, MiMo-V2.6-Pro leads 46 to 45.
| GLM-5.3 · Z.ai | MiMo-V2.6-Pro · Xiaomi | |
|---|---|---|
| Leaderboard rank | #5 | #7 |
| Score (0–100) | 81 | 81 |
| Tier | Open weights | Open weights |
| Price per 1M tokens | $1.40 in · $4.40 out | $0.43 in · $0.87 out |
| Context window | 1M | 1M |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 45 | 46 |
| Arena Text score | 1478 (rank 27) | 1480 (rank 26) |
| Scale SWE-Bench Pro V2 (mini-swe-agent) | 84.30% | — |
| Scale MCP Atlas | 84.20 | — |
| Arena WebDev score | 1622 (rank 22) | 1629 (rank 19) |
| Best for | Agentic coding in Claude Code / Pi-style harnesses, Security research and code auditing, Self-hosted frontier-ish reasoning | Self-hosted agentic coding, Cheap frontier-class batch reasoning, Replacing closed mid-tier models |
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 MiMo-V2.6-Pro if…
- you need self-hosted agentic coding
- you need cheap frontier-class batch reasoning
- you need replacing closed mid-tier models
The strongest open-weight model right now and absurdly cheap for what it does. MIT-licensed 1T MoE that lands within a few points of Muse Spark and Grok 4.7. Slow-ish and verbose, so budget for latency and thinking tokens.
Caveats: Overthinks: long, repetitive reasoning traces on simple edits; AA flags it as verbose vs the median model. ~47 tok/s on the first-party API is slow next to Gemini Flash or Haiku-class models. Only 4k Arena votes so far; the rank is still settling.
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.
MiMo-V2.6-Pro: HN (1,130 points) was won over less by scores than by Xiaomi's transparency: a live training dashboard with loss curves, dropped datasets and running cost estimates. Users call it 'incredibly cheap' for Muse-Spark-level benchmarks and say it keeps DeepSeek and GLM 'in check'. Complaints are consistent: 'faster than DeepSeek but still much slower than leading models', and it 'seems to overthink way too much', producing huge reasoning traces for trivial edits. Several recommend it via OpenRouter rather than the Chinese first-party API.
Questions people ask
Which is better, GLM-5.3 or MiMo-V2.6-Pro?
GLM-5.3 and MiMo-V2.6-Pro are within three points on the Models at Work leaderboard, which we treat as a tie (81 vs 81). MiMo-V2.6-Pro is about 4.0× cheaper per blended million tokens ($0.54 vs $2.15). On Artificial Analysis' independent Intelligence Index, MiMo-V2.6-Pro leads 46 to 45.
When should I choose GLM-5.3 over MiMo-V2.6-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 MiMo-V2.6-Pro over GLM-5.3?
Choose MiMo-V2.6-Pro for self-hosted agentic coding, cheap frontier-class batch reasoning, replacing closed mid-tier models. The strongest open-weight model right now and absurdly cheap for what it does. MIT-licensed 1T MoE that lands within a few points of Muse Spark and Grok 4.7. Slow-ish and verbose, so budget for latency and thinking tokens.
Which is cheaper, GLM-5.3 or MiMo-V2.6-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 Oct 11, 2026). MiMo-V2.6-Pro costs $0.43 per million input tokens and $0.87 per million output tokens on Xiaomi's list price (MiMo-V2.6-Pro - Artificial Analysis, read Oct 11, 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.