Llama 4 Scout 17B 16E Instruct vs GLM 5.3 Flash
At a Glance
| Compare |  GLM 5.3 FlashZ.ai | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #2 of 44$0.0087 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 21, 2026 | $0.075Z.ai ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 21, 2026 | $0.25Z.ai ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 10,000K | 1,000K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 2, 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-Flash |
|---|---|---|
| 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 | 1,471.89100% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,117.8086% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114 | 1,298.71100% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 47.3254% of row best · points · Llama 4 Scout · 696 output tokens / case | 88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 2 benchmark winsNo 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-Flash |
|---|---|---|
| Developer | Meta | Z.ai |
| Family | Llama 4 Scout 17b 16e Instruct | Glm 5 3 Flash |
| Model | Llama-4-Scout-17B-16E-Instruct | GLM-5.3-Flash |
| Version | Llama-4-Scout-17B-16E-Instruct | GLM-5.3-Flash |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 10,000K | 1,000K |
| Total parameters | 108.6B | 320B |
| Active parameters | 17B | 18B |
| License | other | MIT |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| 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, computer-use, reasoning, structured_outputs, tools, vision |
Llama 4 Scout 17B 16E Instruct Capabilities
GLM 5.3 Flash Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E Instruct vs GLM 5.3 Flash FAQs
Is Llama 4 Scout 17B 16E Instruct or GLM 5.3 Flash 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 Flash, 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 Flash?+
Llama 4 Scout 17B 16E Instruct is $0.10 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Llama 4 Scout 17B 16E Instruct is $0.30 and GLM 5.3 Flash is $0.25 per million tokens, so GLM 5.3 Flash is cheaper on this metric.
Which has a larger context window, Llama 4 Scout 17B 16E Instruct or GLM 5.3 Flash?+
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 Flash supports 1,000K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or GLM 5.3 Flash?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Llama 4 Scout 17B 16E Instruct or GLM 5.3 Flash be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E Instruct is open weight; GLM 5.3 Flash is open weight.
Can Llama 4 Scout 17B 16E Instruct and GLM 5.3 Flash understand images?+
Llama 4 Scout 17B 16E Instruct is documented with image input; GLM 5.3 Flash is 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 Flash?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and GLM 5.3 Flash is 131K.
Do Llama 4 Scout 17B 16E Instruct and GLM 5.3 Flash support reasoning and tool use?+
Llama 4 Scout 17B 16E Instruct: tool calling and image input. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Scout 17B 16E Instruct or GLM 5.3 Flash?+
Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; GLM 5.3 Flash has 4, a tie.
Which offers better value, Llama 4 Scout 17B 16E Instruct or GLM 5.3 Flash?+
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.