API cost decision in 10 seconds

Qwen3.5 397B A17B vs Qwen3 VL 235B A22B Thinking

The standard workload cost is tied; choose by context window, provider fit, latency, or model quality.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-31T01:07:53Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

The standard workload cost is tied; choose by context window, provider fit, latency, or model quality.

Both models are estimated at $1.56 for the standard 1M input plus 500K output workload.

Cost-first pickTie

Context-first pickQwen3.5 397B A17B

Sample savings$00%

10x traffic gap$0

Context-window winner: Qwen3.5 397B A17B. Cost does not separate this pair on the standard workload, so the next decision point is context window and model behavior.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors Qwen3 VL 235B A22B Thinking, balanced workload favors Qwen3.5 397B A17B, and output-heavy chatbot favors Qwen3.5 397B A17B.

Workload shape	Token mix	Better pick	Qwen3.5 397B A17B	Qwen3 VL 235B A22B Thinking
Input-heavy / RAG	5M input + 500K output	Qwen3 VL 235B A22B Thinking	$3.12	$2.6
Balanced workload	1M input + 1M output	Qwen3.5 397B A17B	$2.73	$2.86
Output-heavy chatbot	1M input + 5M output	Qwen3.5 397B A17B	$12.09	$13.26

Cheaper input Qwen3 VL 235B A22B Thinking $0.39 vs $0.26 / 1M

Qwen3 VL 235B A22B Thinking is $0.13 cheaper per 1M input tokens (33.3% lower; 1.5x difference).

Cheaper output Qwen3.5 397B A17B $2.34 vs $2.6 / 1M

Qwen3.5 397B A17B is $0.26 cheaper per 1M output tokens (10% lower; 1.11x difference).

Larger context Qwen3.5 397B A17B 262.14K vs 131.07K

Qwen3.5 397B A17B has 131.07K more context (2x larger).

Sample workload Tie $1.56 vs $1.56

Both models have the same estimated cost for the standard 1M input plus 500K output workload: $1.56.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Qwen3.5 397B A17B Calculating… Estimated API cost

Qwen3 VL 235B A22B Thinking 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 235B A22B Thinking has the lower input price; Qwen3.5 397B A17B has the lower output price; Qwen3.5 397B A17B offers the larger context window. For the 1M input plus 500K output sample, the standard workload cost is tied.

For a 1M input token plus 500K output token workload, the estimated API cost is $1.56 for Qwen3.5 397B A17B and $1.56 for Qwen3 VL 235B A22B Thinking.

Best Fit

Choose Qwen3.5 397B A17B when you care most about lower output-token price, and larger context window.

Choose Qwen3 VL 235B A22B Thinking when you care most about lower input-token price.

Decision Notes

Both models are estimated at $1.56 for the standard 1M input plus 500K output workload.
Both models have the same estimated cost for the standard 1M input plus 500K output workload: $1.56.
Qwen3 VL 235B A22B Thinking is $0.13 cheaper per 1M input tokens (33.3% lower; 1.5x difference).
Qwen3.5 397B A17B is $0.26 cheaper per 1M output tokens (10% lower; 1.11x difference).
Qwen3.5 397B A17B has 131.07K more context (2x larger).

Head-to-Head Specs

Feature	Qwen3.5 397B A17B (Qwen)	Qwen3 VL 235B A22B Thinking (Qwen)
Input Price prompt tokens per 1M	$0.39	$0.26
Completion Price per 1M tokens	$2.34	$2.6
Sample Workload Cost 1M input + 500K output	$1.56	$1.56
Context Window	262.14K	131.07K
Release Date	2026-02-16	2025-09-23
Popularity	#89	#103

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Tie	Both models are estimated at $1.56 for the standard 1M input plus 500K output workload.
High-volume input processing	Qwen3 VL 235B A22B Thinking	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Qwen3.5 397B A17B	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Qwen3.5 397B A17B	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 Qwen3.5 397B A17B when lower sample workload cost matters most: $0.
Qwen3 Coder 480B A35B (free) can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.
Qwen2.5 7B Instruct can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.09.
Qwen3.5-9B can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.11.

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.
MiMo-V2.5 offers 1.05M context with $0.28 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.

Popular competitors

Hy3 preview · Tencent · #1
MiMo-V2.5 · Xiaomi · #2
DeepSeek V4 Flash · DeepSeek · #3
Owl Alpha · OpenRouter · #4

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.

Open largest context models

Provider catalogs

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

Open provider hubs

Qwen catalog

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

Open Qwen models

Qwen3.5 397B A17B

The Qwen3.5 series 397B-A17B 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. It delivers...

Qwen3 VL 235B A22B Thinking

Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....