AionLabs: Aion 3.5 Mini vs Qwen: Qwen3.8 2.4T A95B (batch)

Neither wins outright: AionLabs: Aion 3.5 Mini is the pick when cost matters at scale, Qwen: Qwen3.8 2.4T A95B (batch) when you need to fit long inputs in one request.

Key numbers

SpecAionLabs: Aion 3.5 MiniQwen: Qwen3.8 2.4T A95B (batch)
ProviderAion LabsQwen
Input price (per 1M tokens)$0.70$2.00
Output price (per 1M tokens)$1.40$6.00
Blended price (3:1 input/output)$0.87$3.00
Cheaper than … of all models we track42%21%
Context window262K tokens (~393 pages)1.0M tokens (~1,515 pages)
Larger context than … of all models48%79%
Max output per response33K tokens (~24,600 words)909K tokens (~681,800 words)
Input typestexttext
Output typestexttext
Free variantNoNo
Listed since2026-09-232026-08-12
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What it costs for real workloads

Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.

Workload (per month)AionLabs: Aion 3.5 MiniQwen: Qwen3.8 2.4T A95B (batch)Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$80.50$270AionLabs: Aion 3.5 Mini saves $190
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$98.00$300AionLabs: Aion 3.5 Mini saves $202
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$101$312AionLabs: Aion 3.5 Mini saves $211
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$64.40$196AionLabs: Aion 3.5 Mini saves $132
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$36.40$106AionLabs: Aion 3.5 Mini saves $69.60

Capabilities side by side

CapabilityAionLabs: Aion 3.5 MiniQwen: Qwen3.8 2.4T A95B (batch)What it lets you do
Tool / function calling✓ Yes✓ Yescall your APIs and run agent loops
Structured output (JSON schema)✓ Yes✓ Yesreturn JSON that validates against your schema
Reasoning / thinking mode✓ Yes✓ Yesspend extra tokens thinking before answering hard problems
Image input——read screenshots, charts and scanned pages
Audio input——take speech or audio directly
File / PDF input——accept documents without your own parsing step
Image output——generate images, not just text
Built-in web search——answer from live web results
Seed (reproducible sampling)——repeat a generation for tests and evals
Log-probabilities—✓ Yesread token confidence for classification and scoring

From each model's published parameters and input/output types. “—” means not advertised, not proven absent.

Price history

DateModelInput / 1MOutput / 1M
2026-09-02Qwen: Qwen3.8 2.4T A95B (batch)$2.00$6.00

Which should you choose?

Choose AionLabs: Aion 3.5 Mini if…

  • cost matters at scale — it is 71% cheaper on a typical 3:1 input/output mix ($80.50 vs $270 a month for 50,000 support chats)

Choose Qwen: Qwen3.8 2.4T A95B (batch) if…

  • you need to fit long inputs in one request — 1.0M tokens is roughly 1,515 pages, against 393
  • you need log-probabilities — to read token confidence for classification and scoring; AionLabs: Aion 3.5 Mini does not advertise it
  • you generate long outputs in one go — up to 909K tokens (about 681,800 words) per response

Frequently asked questions

Is AionLabs: Aion 3.5 Mini better than Qwen: Qwen3.8 2.4T A95B (batch)?

Neither wins outright: AionLabs: Aion 3.5 Mini is the pick when cost matters at scale, Qwen: Qwen3.8 2.4T A95B (batch) when you need to fit long inputs in one request. In short: AionLabs: Aion 3.5 Mini when cost matters at scale; Qwen: Qwen3.8 2.4T A95B (batch) when you need to fit long inputs in one request, you need log-probabilities, you generate long outputs in one go.

Which is cheaper, AionLabs: Aion 3.5 Mini or Qwen: Qwen3.8 2.4T A95B (batch)?

AionLabs: Aion 3.5 Mini is cheaper: $0.87 vs $3.00 per 1M tokens at a typical 3:1 input/output mix, 71% less. For 50,000 support conversations a month that is $80.50 against $270.

How much do AionLabs: Aion 3.5 Mini and Qwen: Qwen3.8 2.4T A95B (batch) cost per million tokens?

AionLabs: Aion 3.5 Mini costs $0.70 per 1M input tokens and $1.40 per 1M output tokens; Qwen: Qwen3.8 2.4T A95B (batch) costs $2.00 input and $6.00 output. Output tokens are billed separately, so long answers shift the gap.

Which has the bigger context window, AionLabs: Aion 3.5 Mini or Qwen: Qwen3.8 2.4T A95B (batch)?

Qwen: Qwen3.8 2.4T A95B (batch): 1.0M tokens, about 1,515 pages, against 262K (about 393 pages) for AionLabs: Aion 3.5 Mini.

Do AionLabs: Aion 3.5 Mini and Qwen: Qwen3.8 2.4T A95B (batch) support function calling and JSON output?

Tool calling — AionLabs: Aion 3.5 Mini: yes, Qwen: Qwen3.8 2.4T A95B (batch): yes. JSON-schema structured output — AionLabs: Aion 3.5 Mini: yes, Qwen: Qwen3.8 2.4T A95B (batch): yes.

Can AionLabs: Aion 3.5 Mini or Qwen: Qwen3.8 2.4T A95B (batch) read images?

Neither accepts image input — both are text-in models.

Is AionLabs: Aion 3.5 Mini or Qwen: Qwen3.8 2.4T A95B (batch) free to use?

No free variant is listed for either; both are pay-per-token.

What is the maximum output length of AionLabs: Aion 3.5 Mini and Qwen: Qwen3.8 2.4T A95B (batch)?

AionLabs: Aion 3.5 Mini can write up to 33K tokens (about 24,600 words) per response; Qwen: Qwen3.8 2.4T A95B (batch) up to 909K (about 681,800 words).

When were AionLabs: Aion 3.5 Mini and Qwen: Qwen3.8 2.4T A95B (batch) released?

AionLabs: Aion 3.5 Mini was listed on 2026-09-23; Qwen: Qwen3.8 2.4T A95B (batch) on 2026-08-12.

Has the price of AionLabs: Aion 3.5 Mini or Qwen: Qwen3.8 2.4T A95B (batch) changed?

AionLabs: Aion 3.5 Mini: price unchanged since we started tracking it on 2026-09-24. Qwen: Qwen3.8 2.4T A95B (batch): input price cut 20% ($2.50 → $2.00/1M), output price cut 4% ($6.25 → $6.00/1M) on 2026-09-02.

About the two models

Compare AionLabs: Aion 3.5 Mini with other models

Compare Qwen: Qwen3.8 2.4T A95B (batch) with other models

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