API cost decision in 10 seconds

Qwen3.6 Max Preview vs Qwen3.5-122B-A10B

Pick Qwen3.5-122B-A10B when budget is the priority.

Pricing data updated:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3.5-122B-A10B when budget is the priority.

On the standard 1M input plus 500K output workload, Qwen3.5-122B-A10B is estimated at $1.3 vs $4.16 for Qwen3.6 Max Preview, saving $2.86 (68.8% lower).

Cost-first pickQwen3.5-122B-A10B
Context-first pickBoth models
Sample savings$2.8668.8%
10x traffic gap$28.6

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $28.6. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen3.5-122B-A10B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickQwen3.6 Max PreviewQwen3.5-122B-A10B
Input-heavy / RAG5M input + 500K outputQwen3.5-122B-A10B$8.32$2.34
Balanced workload1M input + 1M outputQwen3.5-122B-A10B$7.28$2.34
Output-heavy chatbot1M input + 5M outputQwen3.5-122B-A10B$32.24$10.66
Cheaper input Qwen3.5-122B-A10B $1.04 vs $0.26 / 1M

Qwen3.5-122B-A10B is $0.78 cheaper per 1M input tokens (75% lower; 4x difference).

Cheaper output Qwen3.5-122B-A10B $6.24 vs $2.08 / 1M

Qwen3.5-122B-A10B is $4.16 cheaper per 1M output tokens (66.7% lower; 3x difference).

Larger context Tie 262.14K vs 262.14K

Both models report the same context window at 262.14K tokens.

Sample workload Qwen3.5-122B-A10B $4.16 vs $1.3

Qwen3.5-122B-A10B is $2.86 cheaper on the standard workload (68.8% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Qwen3.6 Max Preview Calculating… Estimated API cost
Qwen3.5-122B-A10B 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

Qwen3.5-122B-A10B has the lower input price; Qwen3.5-122B-A10B has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Qwen3.5-122B-A10B is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $4.16 for Qwen3.6 Max Preview and $1.3 for Qwen3.5-122B-A10B.

Best Fit

Choose Qwen3.6 Max Preview when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Choose Qwen3.5-122B-A10B when you care most about lower input-token price, and lower output-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Qwen3.5-122B-A10B is estimated at $1.3 vs $4.16 for Qwen3.6 Max Preview, saving $2.86 (68.8% lower).
  • Qwen3.5-122B-A10B is $2.86 cheaper on the standard workload (68.8% lower).
  • Qwen3.5-122B-A10B is $0.78 cheaper per 1M input tokens (75% lower; 4x difference).
  • Qwen3.5-122B-A10B is $4.16 cheaper per 1M output tokens (66.7% lower; 3x difference).
  • Both models report the same context window at 262.14K tokens.
Head-to-Head Specs
FeatureQwen3.6 Max Preview
(Qwen)
Qwen3.5-122B-A10B
(Qwen)
Input Price
prompt tokens per 1M
$1.04$0.26
Completion Price
per 1M tokens
$6.24$2.08
Sample Workload Cost
1M input + 500K output
$4.16$1.3
Context Window262.14K262.14K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionQwen3.5-122B-A10BOn the standard 1M input plus 500K output workload, Qwen3.5-122B-A10B is estimated at $1.3 vs $4.16 for Qwen3.6 Max Preview, saving $2.86 (68.8% lower).
High-volume input processingQwen3.5-122B-A10BLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsQwen3.5-122B-A10BLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workTieA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

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Cheaper alternatives

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Larger context alternatives

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Provider catalogs

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Open provider hubs

Qwen catalog

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

Open Qwen models
Qwen3.6 Max Preview

Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...

Qwen3.5-122B-A10B

The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...