API cost decision in 10 seconds

Llama 3.1 70B Instruct vs Qwen3 VL 30B A3B Instruct

Pick Qwen3 VL 30B A3B Instruct when budget and context both matter.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T19:08:13Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3 VL 30B A3B Instruct when budget and context both matter.

On the standard 1M input plus 500K output workload, Qwen3 VL 30B A3B Instruct is estimated at $0.39 vs $0.6 for Llama 3.1 70B Instruct, saving $0.21 (35% lower).

Cost-first pickQwen3 VL 30B A3B Instruct

Context-first pickQwen3 VL 30B A3B Instruct

Sample savings$0.2135%

10x traffic gap$2.1

Qwen3 VL 30B A3B Instruct is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $2.1. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors Qwen3 VL 30B A3B Instruct, balanced workload favors Qwen3 VL 30B A3B Instruct, and output-heavy chatbot favors Llama 3.1 70B Instruct.

Workload shape	Token mix	Better pick	Llama 3.1 70B Instruct	Qwen3 VL 30B A3B Instruct
Input-heavy / RAG	5M input + 500K output	Qwen3 VL 30B A3B Instruct	$2.2	$0.91
Balanced workload	1M input + 1M output	Qwen3 VL 30B A3B Instruct	$0.8	$0.65
Output-heavy chatbot	1M input + 5M output	Llama 3.1 70B Instruct	$2.4	$2.73

Cheaper input Qwen3 VL 30B A3B Instruct $0.4 vs $0.13 / 1M

Qwen3 VL 30B A3B Instruct is $0.27 cheaper per 1M input tokens (67.5% lower; 3.08x difference).

Cheaper output Llama 3.1 70B Instruct $0.4 vs $0.52 / 1M

Llama 3.1 70B Instruct is $0.12 cheaper per 1M output tokens (23.1% lower; 1.3x difference).

Larger context Qwen3 VL 30B A3B Instruct 131.07K vs 262.14K

Qwen3 VL 30B A3B Instruct has 131.07K more context (2x larger).

Sample workload Qwen3 VL 30B A3B Instruct $0.6 vs $0.39

Qwen3 VL 30B A3B Instruct is $0.21 cheaper on the standard workload (35% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Llama 3.1 70B Instruct Calculating… Estimated API cost

Qwen3 VL 30B A3B Instruct Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Qwen3 VL 30B A3B Instruct has the lower input price; Llama 3.1 70B Instruct has the lower output price; Qwen3 VL 30B A3B Instruct offers the larger context window. For the 1M input plus 500K output sample, Qwen3 VL 30B A3B Instruct is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.6 for Llama 3.1 70B Instruct and $0.39 for Qwen3 VL 30B A3B Instruct.

Best Fit

Choose Llama 3.1 70B Instruct when you care most about lower output-token price.

Choose Qwen3 VL 30B A3B Instruct when you care most about lower input-token price, and larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, Qwen3 VL 30B A3B Instruct is estimated at $0.39 vs $0.6 for Llama 3.1 70B Instruct, saving $0.21 (35% lower).
Qwen3 VL 30B A3B Instruct is $0.21 cheaper on the standard workload (35% lower).
Qwen3 VL 30B A3B Instruct is $0.27 cheaper per 1M input tokens (67.5% lower; 3.08x difference).
Llama 3.1 70B Instruct is $0.12 cheaper per 1M output tokens (23.1% lower; 1.3x difference).
Qwen3 VL 30B A3B Instruct has 131.07K more context (2x larger).

Head-to-Head Specs

Feature	Llama 3.1 70B Instruct (Meta)	Qwen3 VL 30B A3B Instruct (Qwen)
Input Price prompt tokens per 1M	$0.4	$0.13
Completion Price per 1M tokens	$0.4	$0.52
Sample Workload Cost 1M input + 500K output	$0.6	$0.39
Context Window	131.07K	262.14K
Release Date	2024-07-23	2025-10-06
Popularity	#87	#116

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Qwen3 VL 30B A3B Instruct	On the standard 1M input plus 500K output workload, Qwen3 VL 30B A3B Instruct is estimated at $0.39 vs $0.6 for Llama 3.1 70B Instruct, saving $0.21 (35% lower).
High-volume input processing	Qwen3 VL 30B A3B Instruct	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Llama 3.1 70B Instruct	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Qwen3 VL 30B A3B Instruct	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Llama 3.3 70B Instruct (free) can replace Llama 3.1 70B Instruct when lower sample workload cost matters most: $0.
Llama 3.2 3B Instruct (free) can replace Llama 3.1 70B Instruct when lower sample workload cost matters most: $0.
Llama 3.1 8B Instruct can replace Llama 3.1 70B Instruct when lower sample workload cost matters most: $0.04.
Llama 3 8B Instruct can replace Llama 3.1 70B Instruct when lower sample workload cost matters most: $0.06.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Owl Alpha offers 1.05M context with $0 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
Gemini 2.5 Flash Lite offers 1.05M context with $0.3 sample workload cost.

Popular competitors

DeepSeek V4 Flash · DeepSeek · #1
Hy3 preview · Tencent · #2
Claude Opus 4.7 · Anthropic · #3
Claude Sonnet 4.6 · Anthropic · #4

Cheaper alternatives

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Larger context alternatives

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Provider catalogs

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Meta catalog

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Qwen catalog

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Open Qwen models

Llama 3.1 70B Instruct

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...

Qwen3 VL 30B A3B Instruct

Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...