API cost decision in 10 seconds

Qwen3 235B A22B Thinking 2507 vs Qwen2.5 VL 72B Instruct

Pick Qwen2.5 VL 72B Instruct for lower cost; pick Qwen3 235B A22B Thinking 2507 only if the larger context window matters more.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T18:08:02Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen2.5 VL 72B Instruct for lower cost; pick Qwen3 235B A22B Thinking 2507 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $0.9 for Qwen3 235B A22B Thinking 2507, saving $0.27 (30.3% lower).

Cost-first pickQwen2.5 VL 72B Instruct

Context-first pickQwen3 235B A22B Thinking 2507

Sample savings$0.2730.3%

10x traffic gap$2.72

Qwen3 235B A22B Thinking 2507 has more context, but Qwen2.5 VL 72B Instruct saves $0.27 on the standard workload. At 10x that traffic, the same price gap is about $2.72. 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 235B A22B Thinking 2507, balanced workload favors Qwen2.5 VL 72B Instruct, and output-heavy chatbot favors Qwen2.5 VL 72B Instruct.

Workload shape	Token mix	Better pick	Qwen3 235B A22B Thinking 2507	Qwen2.5 VL 72B Instruct
Input-heavy / RAG	5M input + 500K output	Qwen3 235B A22B Thinking 2507	$1.5	$1.62
Balanced workload	1M input + 1M output	Qwen2.5 VL 72B Instruct	$1.64	$1
Output-heavy chatbot	1M input + 5M output	Qwen2.5 VL 72B Instruct	$7.62	$4

Cheaper input Qwen3 235B A22B Thinking 2507 $0.1495 vs $0.25 / 1M

Qwen3 235B A22B Thinking 2507 is $0.1 cheaper per 1M input tokens (40.2% lower; 1.67x difference).

Cheaper output Qwen2.5 VL 72B Instruct $1.495 vs $0.75 / 1M

Qwen2.5 VL 72B Instruct is $0.75 cheaper per 1M output tokens (49.8% lower; 1.99x difference).

Larger context Qwen3 235B A22B Thinking 2507 262.14K vs 131.07K

Qwen3 235B A22B Thinking 2507 has 131.07K more context (2x larger).

Sample workload Qwen2.5 VL 72B Instruct $0.9 vs $0.62

Qwen2.5 VL 72B Instruct is $0.27 cheaper on the standard workload (30.3% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Qwen3 235B A22B Thinking 2507 Calculating… Estimated API cost

Qwen2.5 VL 72B 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 235B A22B Thinking 2507 has the lower input price; Qwen2.5 VL 72B Instruct has the lower output price; Qwen3 235B A22B Thinking 2507 offers the larger context window. For the 1M input plus 500K output sample, Qwen2.5 VL 72B Instruct is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.9 for Qwen3 235B A22B Thinking 2507 and $0.62 for Qwen2.5 VL 72B Instruct.

Best Fit

Choose Qwen3 235B A22B Thinking 2507 when you care most about lower input-token price, and larger context window.

Choose Qwen2.5 VL 72B Instruct when you care most about lower output-token price.

Decision Notes

On the standard 1M input plus 500K output workload, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $0.9 for Qwen3 235B A22B Thinking 2507, saving $0.27 (30.3% lower).
Qwen2.5 VL 72B Instruct is $0.27 cheaper on the standard workload (30.3% lower).
Qwen3 235B A22B Thinking 2507 is $0.1 cheaper per 1M input tokens (40.2% lower; 1.67x difference).
Qwen2.5 VL 72B Instruct is $0.75 cheaper per 1M output tokens (49.8% lower; 1.99x difference).
Qwen3 235B A22B Thinking 2507 has 131.07K more context (2x larger).

Head-to-Head Specs

Feature	Qwen3 235B A22B Thinking 2507 (Qwen)	Qwen2.5 VL 72B Instruct (Qwen)
Input Price prompt tokens per 1M	$0.1495	$0.25
Completion Price per 1M tokens	$1.495	$0.75
Sample Workload Cost 1M input + 500K output	$0.9	$0.62
Context Window	262.14K	131.07K
Release Date	2025-07-25	2025-02-01
Popularity	#133	#150

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Qwen2.5 VL 72B Instruct	On the standard 1M input plus 500K output workload, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $0.9 for Qwen3 235B A22B Thinking 2507, saving $0.27 (30.3% lower).
High-volume input processing	Qwen3 235B A22B Thinking 2507	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Qwen2.5 VL 72B Instruct	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Qwen3 235B A22B Thinking 2507	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 235B A22B Thinking 2507 when lower sample workload cost matters most: $0.
Qwen3 Coder 480B A35B (free) can replace Qwen3 235B A22B Thinking 2507 when lower sample workload cost matters most: $0.
Qwen2.5 7B Instruct can replace Qwen3 235B A22B Thinking 2507 when lower sample workload cost matters most: $0.09.
Qwen3.5-9B can replace Qwen3 235B A22B Thinking 2507 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.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
DeepSeek V4 Pro offers 1.05M context with $0.87 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

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 235B A22B Thinking 2507

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

Qwen2.5 VL 72B Instruct

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.