Llama 3.3 70B 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 | $0.10Openrouter ↗ · Sep 22, 2026 | $1.20Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $0.32Openrouter ↗ · Sep 22, 2026 | $4.00Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 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-3.3-70B-Instruct | GLM-5.3 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,273.9886% of row best · rating · llama-3.3-70b-instruct; 95% CI [1270.49383309, 1277.45726344]; votes 54412; rank 262 | 1,475.08100% of row best · rating · glm-5.3-max; 95% CI [1468.67816284, 1481.48915679]; votes 10960; rank 20 |
| 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-3.3-70B-Instruct | GLM-5.3 |
|---|---|---|
| Developer | Meta | Z.ai |
| Family | Llama 3 3 70b Instruct | Glm 5 3 |
| Model | Llama-3.3-70B-Instruct | GLM-5.3 |
| Version | Llama-3.3-70B-Instruct | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2024-12-06 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 1,000K |
| Total parameters | 70.6B | Unknown |
| Active parameters | Unknown | Unknown |
| License | llama3.3 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | 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 3.3 70B Instruct Capabilities
GLM 5.3 Capabilities
Related Comparisons
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|---|---|---|---|
| vs | GLM-5.2Z.ai | cross-developer peerstext | |
| vs |  GPT-6 AstraOpenAI | cross-developer peerstext | |
Claude Fable 5.1Anthropic | vs | cross-developer peerstext | |
 Gemini 3.1 ProGoogle DeepMind | vs | cross-developer peerstext | |
 DeepSeek-V4.1-FlashDeepSeek | vs | cross-developer peerstext | |
| vs | Grok 4.6xAI | cross-developer peerstext | |
| vs |  Qwen3.8-MaxQwen | cross-developer peerstext | |
| vs |  Kimi-K3Moonshot AI | cross-developer peerstext | |
 MiniMax-M3MiniMax | vs | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
Seed 2.1 ProByteDance Seed | vs | cross-developer peerstext | |
| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.3 70B Instruct vs GLM 5.3 FAQs
Is Llama 3.3 70B Instruct or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.3 70B 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 3.3 70B Instruct or GLM 5.3?+
Llama 3.3 70B Instruct is $0.10 and GLM 5.3 is $1.20 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric. Llama 3.3 70B Instruct is $0.32 and GLM 5.3 is $4.00 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric.
Which has a larger context window, Llama 3.3 70B Instruct or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Llama 3.3 70B Instruct supports 131K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Llama 3.3 70B 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 3.3 70B Instruct or GLM 5.3 be self-hosted?+
Llama 3.3 70B Instruct is the only model in this pair currently marked as self-hostable. Llama 3.3 70B Instruct is open weight; GLM 5.3 is not marked open weight.
Can Llama 3.3 70B Instruct and GLM 5.3 understand images?+
Llama 3.3 70B Instruct is not 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 3.3 70B Instruct or GLM 5.3?+
Neither has a larger sourced maximum output. Llama 3.3 70B Instruct is — and GLM 5.3 is 131K.
Do Llama 3.3 70B Instruct and GLM 5.3 support reasoning and tool use?+
Llama 3.3 70B Instruct: tool calling. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.3 70B Instruct or GLM 5.3?+
Llama 3.3 70B Instruct has 3 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, Llama 3.3 70B 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.