GLM-5.3-Flash vs Muse Spark 1.3
GLM-5.3-Flash and Muse Spark 1.3 are within three points on the Models at Work leaderboard, which we treat as a tie (75 vs 75). GLM-5.3-Flash is about 3.3× cheaper per blended million tokens ($0.24 vs $0.78, one figure an Artificial Analysis estimate).
| GLM-5.3-Flash · Z.ai | Muse Spark 1.3 · Meta | |
|---|---|---|
| Leaderboard rank | #9 | #10 |
| Score (0–100) | 75 | 75 |
| Tier | Open weights | Workhorse |
| Price per 1M tokens | $0.15 in · $0.50 out | $0.78 blended (estimate) |
| Context window | 1M | Not verified |
| Open weights | Yes | Not verified |
| Artificial Analysis Intelligence Index | 28 | — |
| Arena Text score | 1475 (rank 38) | — |
| Arena WebDev score | 1609 (rank 26) | — |
| Arena text score | — | 1494 |
| DistressBench (Scale) | — | 84.88 |
| Best for | Cheap agentic coding execution, Bulk document and image processing, Self-hosted multimodal assistants | Latency-first products, Customer-facing chat, Meta-ecosystem teams |
Compare source-qualified API rates and a monthly workload example before estimating your bill.
Choose GLM-5.3-Flash if…
- you need cheap agentic coding execution
- you need bulk document and image processing
- you need self-hosted multimodal assistants
Probably the best price-to-intelligence ratio on the market: AA 42 for $0.15/$0.50, MIT-licensed, natively multimodal, 1M context. Not actually fast despite the name; treat it as a cheap executor behind a stronger planner.
Caveats: 'Too slow for execution, despite the name' on Z.ai's own hosting (~60 tok/s); third-party hosts vary. Users report less consistent output than full GLM-5.3; pair it with a planning model. 320B total params: self-hosting still needs a multi-GPU node.
Choose Muse Spark 1.3 if…
- you need latency-first products
- you need customer-facing chat
- you need meta-ecosystem teams
The fastest thing in the table by a distance, and the one to beat on the stress benchmark nobody else talks about.
Caveats: Artificial Analysis re-based its Intelligence Index by 2 Nov 2026 and no longer lists this model on the new scale (old figure 48, read 2026-10-19); it is dropped from the score until re-measured. Rank rests on Arena alone.
What practitioners say
GLM-5.3-Flash: A month-long HN diary ('One month coding with GLM 5.3 Flash', 233 points) spent $68 total; commenters agreed 'a month of agentic coding for $68 is the headline'. The working pattern is 'GLM-5.3 to write the plan, Flash to implement', with one user noting that given an unambiguous plan 'GLM 5.3 Flash executes it just fine'. Negatives: 'too slow for execution, despite the name' on Z.ai hosting, and the full model is 'way more consistent'.
Questions people ask
Which is better, GLM-5.3-Flash or Muse Spark 1.3?
GLM-5.3-Flash and Muse Spark 1.3 are within three points on the Models at Work leaderboard, which we treat as a tie (75 vs 75). GLM-5.3-Flash is about 3.3× cheaper per blended million tokens ($0.24 vs $0.78, one figure an Artificial Analysis estimate).
When should I choose GLM-5.3-Flash over Muse Spark 1.3?
Choose GLM-5.3-Flash for cheap agentic coding execution, bulk document and image processing, self-hosted multimodal assistants. Probably the best price-to-intelligence ratio on the market: AA 42 for $0.15/$0.50, MIT-licensed, natively multimodal, 1M context. Not actually fast despite the name; treat it as a cheap executor behind a stronger planner.
When should I choose Muse Spark 1.3 over GLM-5.3-Flash?
Choose Muse Spark 1.3 for latency-first products, customer-facing chat, meta-ecosystem teams. The fastest thing in the table by a distance, and the one to beat on the stress benchmark nobody else talks about.
Which is cheaper, GLM-5.3-Flash or Muse Spark 1.3?
GLM-5.3-Flash costs $0.15 per million input tokens and $0.50 per million output tokens on Z.ai's list price (Z.ai Pricing, read Nov 2, 2026). Meta does not publish a simple list price for Muse Spark 1.3; Artificial Analysis estimates a blended $0.78 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.