GLM-5.3-Flash vs Mistral Small 4
GLM-5.3-Flash scores higher on the Models at Work leaderboard (79 vs 42 out of 100). They cost about the same per million tokens ($0.24 vs $0.26 blended). On Artificial Analysis' independent Intelligence Index, GLM-5.3-Flash leads 42 to 11.
| GLM-5.3-Flash · Z.ai | Mistral Small 4 · Mistral AI | |
|---|---|---|
| Leaderboard rank | #9 | #24 |
| Score (0–100) | 79 | 42 |
| Tier | Open weights | Open weights |
| Price per 1M tokens | $0.15 in · $0.50 out | $0.15 in · $0.60 out |
| Context window | 1M | 256K |
| Open weights | Yes | Yes |
| Artificial Analysis Intelligence Index | 42 | 11 |
| Arena Text score | 1475 (rank 38) | — |
| Arena WebDev score | 1609 (rank 26) | — |
| Best for | Cheap agentic coding execution, Bulk document and image processing, Self-hosted multimodal assistants | Self-hosted multimodal chat on one GPU, Fine-tuning base for EU-regulated teams, Cheap fast tasks with a reasoning_effort dial |
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 Mistral Small 4 if…
- you need self-hosted multimodal chat on one gpu
- you need fine-tuning base for eu-regulated teams
- you need cheap fast tasks with a reasoning_effort dial
One Apache-2.0 model replacing Magistral, Pixtral and Devstral: 119B MoE with 6B active, so it fits one H100 at 4-bit and runs at ~165 tok/s. $0.15/$0.60 is hard to beat for a self-hostable multimodal model, but it is not clever.
Caveats: AA 11; HN testers rate it 'okay, nothing exceptional' and 'not the best' for complex agentic work. Not listed on Arena; limited independent evaluation. Mistral Medium 3.5 (Modified MIT) is the step up if you need more intelligence from the same vendor.
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'.
Mistral Small 4: Modest HN reception (126 points). Fans liked the economics: '$0.60/1M output is a steal' versus Qwen models that 'waste tokens on reasoning', and that ~120B 'fits onto a single H100 with 4 bit quant'. Others were blunt: 'okay, nothing exceptional', 'I'd use it for some basic tasks but not actual complex tasks', and one tester called it 'worse than glm air 4.5'. A later comment found the 4-bit quant roughly comparable to Qwen 3.6 27B.
Questions people ask
Which is better, GLM-5.3-Flash or Mistral Small 4?
GLM-5.3-Flash scores higher on the Models at Work leaderboard (79 vs 42 out of 100). They cost about the same per million tokens ($0.24 vs $0.26 blended). On Artificial Analysis' independent Intelligence Index, GLM-5.3-Flash leads 42 to 11.
When should I choose GLM-5.3-Flash over Mistral Small 4?
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 Mistral Small 4 over GLM-5.3-Flash?
Choose Mistral Small 4 for self-hosted multimodal chat on one gpu, fine-tuning base for eu-regulated teams, cheap fast tasks with a reasoning_effort dial. One Apache-2.0 model replacing Magistral, Pixtral and Devstral: 119B MoE with 6B active, so it fits one H100 at 4-bit and runs at ~165 tok/s. $0.15/$0.60 is hard to beat for a self-hostable multimodal model, but it is not clever.
Which is cheaper, GLM-5.3-Flash or Mistral Small 4?
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 Oct 11, 2026). Mistral Small 4 costs $0.15 per million input tokens and $0.60 per million output tokens on Mistral AI's list price (Mistral Small 4 announcement, 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.