API cost decision in 10 seconds

NewLing 3.0 Flash VL (free) vs Qwen3.8 2.4T A95B (batch)

Pick Ling 3.0 Flash VL (free) for lower cost; pick Qwen3.8 2.4T A95B (batch) only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Ling 3.0 Flash VL (free) for lower cost; pick Qwen3.8 2.4T A95B (batch) only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Ling 3.0 Flash VL (free) is estimated at $0 vs $5 for Qwen3.8 2.4T A95B (batch), saving $5 (100% lower).

Cost-first pickLing 3.0 Flash VL (free)
Context-first pickQwen3.8 2.4T A95B (batch)
Sample savings$5100%
10x traffic gap$50

Qwen3.8 2.4T A95B (batch) has more context, but Ling 3.0 Flash VL (free) saves $5 on the standard workload. At 10x that traffic, the same price gap is about $50. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Ling 3.0 Flash VL (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickLing 3.0 Flash VL (free)Qwen3.8 2.4T A95B (batch)
Input-heavy / RAG5M input + 500K outputLing 3.0 Flash VL (free)$0$13
Balanced workload1M input + 1M outputLing 3.0 Flash VL (free)$0$8
Output-heavy chatbot1M input + 5M outputLing 3.0 Flash VL (free)$0$32
Cheaper input Ling 3.0 Flash VL (free) $0 vs $2 / 1M

Ling 3.0 Flash VL (free) is free for input tokens while Qwen3.8 2.4T A95B (batch) costs $2 per 1M tokens.

Cheaper output Ling 3.0 Flash VL (free) $0 vs $6 / 1M

Ling 3.0 Flash VL (free) is free for output tokens while Qwen3.8 2.4T A95B (batch) costs $6 per 1M tokens.

Larger context Qwen3.8 2.4T A95B (batch) 262.14K vs 1.01M

Qwen3.8 2.4T A95B (batch) has 747.86K more context (3.85x larger).

Sample workload Ling 3.0 Flash VL (free) $0 vs $5

Ling 3.0 Flash VL (free) is free for the standard workload while the other model is estimated at $5.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Ling 3.0 Flash VL (free) Calculating… Estimated API cost
Qwen3.8 2.4T A95B (batch) 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

Ling 3.0 Flash VL (free) has the lower input price; Ling 3.0 Flash VL (free) has the lower output price; Qwen3.8 2.4T A95B (batch) offers the larger context window. For the 1M input plus 500K output sample, Ling 3.0 Flash VL (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Ling 3.0 Flash VL (free) and $5 for Qwen3.8 2.4T A95B (batch).

Best Fit

Choose Ling 3.0 Flash VL (free) when you care most about lower input-token price, and lower output-token price.

Choose Qwen3.8 2.4T A95B (batch) when you care most about larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, Ling 3.0 Flash VL (free) is estimated at $0 vs $5 for Qwen3.8 2.4T A95B (batch), saving $5 (100% lower).
  • Ling 3.0 Flash VL (free) is free for the standard workload while the other model is estimated at $5.
  • Ling 3.0 Flash VL (free) is free for input tokens while Qwen3.8 2.4T A95B (batch) costs $2 per 1M tokens.
  • Ling 3.0 Flash VL (free) is free for output tokens while Qwen3.8 2.4T A95B (batch) costs $6 per 1M tokens.
  • Qwen3.8 2.4T A95B (batch) has 747.86K more context (3.85x larger).
Head-to-Head Specs
FeatureNewLing 3.0 Flash VL (free)
(inclusionAI)
Qwen3.8 2.4T A95B (batch)
(Qwen)
Input Price
prompt tokens per 1M
$0$2
Completion Price
per 1M tokens
$0$6
Sample Workload Cost
1M input + 500K output
$0$5
Context Window262.14K1.01M
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLing 3.0 Flash VL (free)On the standard 1M input plus 500K output workload, Ling 3.0 Flash VL (free) is estimated at $0 vs $5 for Qwen3.8 2.4T A95B (batch), saving $5 (100% lower).
High-volume input processingLing 3.0 Flash VL (free)Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLing 3.0 Flash VL (free)Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workQwen3.8 2.4T A95B (batch)A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • Qwen3 Next 80B A3B Instruct (free) can replace Qwen3.8 2.4T A95B (batch) when lower sample workload cost matters most: $0.
  • Qwen3 Coder 480B A35B (free) can replace Qwen3.8 2.4T A95B (batch) when lower sample workload cost matters most: $0.
  • Qwen3.7 Flash can replace Qwen3.8 2.4T A95B (batch) when lower sample workload cost matters most: $0.1.
  • Qwen3.5-9B can replace Qwen3.8 2.4T A95B (batch) when lower sample workload cost matters most: $0.17.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

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Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

inclusionAI catalog

Review all tracked inclusionAI models before deciding whether this matchup is the right shortlist.

Open inclusionAI models

Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

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Ling 3.0 Flash VL (free)

Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual...

Qwen3.8 2.4T A95B (batch)

Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total. It is...