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Model comparison
Meta's Llama 3.3 70B Instruct against Qwen's Qwen2.5 72B Instruct: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
Llama 3.3 70B Instruct lists at $0.155 per million tokens blended against $0.37 for Qwen2.5 72B Instruct - 2.4x less.
Intelligence index 8 against 8: within a point of each other.
Llama 3.3 70B Instruct reads 131k tokens at once, Qwen2.5 72B Instruct 33k.
Llama 3.3 70B Instruct was released about 3 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Llama 3.3 70B Instruct | Qwen2.5 72B Instruct |
|---|---|---|
| Input price / 1M tokens | $0.10 | $0.36 |
| Output price / 1M tokens | $0.32 | $0.40 |
| Blended (3 in : 1 out) | $0.16 | $0.37 |
| Cached input / 1M | - | - |
| Context window | 131K | 33K |
| Max output | 16K | 16K |
| Input | text | text |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 8 | 8 |
| Coding index | 12 | - |
| Agentic index | - | - |
| Released | Dec 6, 2024 | Sep 19, 2024 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | Llama 3.3 70B Instruct | Qwen2.5 72B Instruct | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $3.60 | $9.20 | $5.60 less on Llama 3.3 70B Instruct |
| Document summaries100M in, 5M out - long inputs, short answers. | $11.60 | $38.00 | $26.40 less on Llama 3.3 70B Instruct |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $12.40 | $29.60 | $17.20 less on Llama 3.3 70B Instruct |
The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: Llama 3.3 70B Instruct wins on cost, and a few bad outcomes on either move the next requests to the other.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: llama-3.3-70b-instruct, kind: llm, metadata: { model: meta-llama/llama-3.3-70b-instruct } }
- { id: qwen-2.5-72b-instruct, kind: llm, metadata: { model: qwen/qwen-2.5-72b-instruct } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [llama-3.3-70b-instruct]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [qwen-2.5-72b-instruct]
weight: 0.8