Llama 4 Scout 17B 16E Instruct vs GLM 5.3
At a Glance
| Compare |  GLM 5.3Z.ai | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #16 of 4670.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.9–80.3 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #34 of 44$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #26 of 3850.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 38.2–54.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 21, 2026 | $1.20Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 21, 2026 | $4.00Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 10,000K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Llama-4-Scout-17B-16E-Instruct | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,279.2987% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.57681953, 1284.00026285]; votes 29740; rank 259 | 1,475.08100% of row best · rating · glm-5.3-max; 95% CI [1468.67816284, 1481.48915679]; votes 10960; rank 20 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 47.3254% of row best · points · Llama 4 Scout · 696 output tokens / case | 88.34100% of row best · points · GLM-5.3 · 22,204 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Llama-4-Scout-17B-16E-Instruct | GLM-5.3 |
|---|---|---|
| Developer | Meta | Z.ai |
| Family | Llama 4 Scout 17b 16e Instruct | Glm 5 3 |
| Model | Llama-4-Scout-17B-16E-Instruct | GLM-5.3 |
| Version | Llama-4-Scout-17B-16E-Instruct | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2025-04-05 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text |
| Output modalities | Text | Text |
| Context window | 10,000K | 1,000K |
| Total parameters | 108.6B | Unknown |
| Active parameters | 17B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation, tools | agents, chat, reasoning, structured_outputs, tools |
Llama 4 Scout 17B 16E Instruct Capabilities
GLM 5.3 Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
| vs | GLM-5.2Z.ai | cross-developer peerstext | |
| vs |  GPT-6 AstraOpenAI | cross-developer peersimage, text | |
Claude Fable 5.1Anthropic | vs | cross-developer peersimage, text | |
 Gemini 3.1 ProGoogle DeepMind | vs | cross-developer peerstext | |
 DeepSeek-V4.1-FlashDeepSeek | vs | cross-developer peersimage, text | |
| vs | Grok 4.6xAI | cross-developer peersimage, text | |
| vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text | |
| vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text | |
 MiniMax-M3MiniMax | vs | cross-developer peersimage, text | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
Seed 2.1 ProByteDance Seed | vs | cross-developer peersimage, text | |
| vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E Instruct vs GLM 5.3 FAQs
Is Llama 4 Scout 17B 16E Instruct or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Scout 17B 16E Instruct or GLM 5.3?+
Llama 4 Scout 17B 16E Instruct is $0.10 and GLM 5.3 is $1.20 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric. Llama 4 Scout 17B 16E Instruct is $0.30 and GLM 5.3 is $4.00 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric.
Which has a larger context window, Llama 4 Scout 17B 16E Instruct or GLM 5.3?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Llama 4 Scout 17B 16E Instruct supports 10,000K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or GLM 5.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 4 Scout 17B 16E Instruct or GLM 5.3 be self-hosted?+
Llama 4 Scout 17B 16E Instruct is the only model in this pair currently marked as self-hostable. Llama 4 Scout 17B 16E Instruct is open weight; GLM 5.3 is not marked open weight.
Can Llama 4 Scout 17B 16E Instruct and GLM 5.3 understand images?+
Llama 4 Scout 17B 16E Instruct is documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Scout 17B 16E Instruct or GLM 5.3?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and GLM 5.3 is 131K.
Do Llama 4 Scout 17B 16E Instruct and GLM 5.3 support reasoning and tool use?+
Llama 4 Scout 17B 16E Instruct: tool calling and image input. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Scout 17B 16E Instruct or GLM 5.3?+
Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; GLM 5.3 has 4, a tie.
Which offers better value, Llama 4 Scout 17B 16E Instruct or GLM 5.3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.