API cost decision in 10 seconds

DeepSeek V3.1 Terminus vs Qwen2.5 VL 72B Instruct

Pick Qwen2.5 VL 72B Instruct for lower cost; pick DeepSeek V3.1 Terminus 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 DeepSeek V3.1 Terminus 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 $0.74 for DeepSeek V3.1 Terminus, saving $0.12 (16.1% lower).

Cost-first pickQwen2.5 VL 72B Instruct

Context-first pickDeepSeek V3.1 Terminus

Sample savings$0.1216.1%

10x traffic gap$1.2

DeepSeek V3.1 Terminus has more context, but Qwen2.5 VL 72B Instruct saves $0.12 on the standard workload. At 10x that traffic, the same price gap is about $1.2. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen2.5 VL 72B Instruct stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	DeepSeek V3.1 Terminus	Qwen2.5 VL 72B Instruct
Input-heavy / RAG	5M input + 500K output	Qwen2.5 VL 72B Instruct	$1.83	$1.62
Balanced workload	1M input + 1M output	Qwen2.5 VL 72B Instruct	$1.22	$1
Output-heavy chatbot	1M input + 5M output	Qwen2.5 VL 72B Instruct	$5.02	$4

Cheaper input Qwen2.5 VL 72B Instruct $0.27 vs $0.25 / 1M

Qwen2.5 VL 72B Instruct is $0.02 cheaper per 1M input tokens (7.4% lower; 1.08x difference).

Cheaper output Qwen2.5 VL 72B Instruct $0.95 vs $0.75 / 1M

Qwen2.5 VL 72B Instruct is $0.2 cheaper per 1M output tokens (21.1% lower; 1.27x difference).

Larger context DeepSeek V3.1 Terminus 163.84K vs 131.07K

DeepSeek V3.1 Terminus has 32.77K more context (1.25x larger).

Sample workload Qwen2.5 VL 72B Instruct $0.74 vs $0.62

Qwen2.5 VL 72B Instruct is $0.12 cheaper on the standard workload (16.1% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

DeepSeek V3.1 Terminus 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

Qwen2.5 VL 72B Instruct has the lower input price; Qwen2.5 VL 72B Instruct has the lower output price; DeepSeek V3.1 Terminus 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 $0.74 for DeepSeek V3.1 Terminus and $0.62 for Qwen2.5 VL 72B Instruct.

Best Fit

Choose DeepSeek V3.1 Terminus when you care most about larger context window.

Choose Qwen2.5 VL 72B Instruct 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, Qwen2.5 VL 72B Instruct is estimated at $0.62 vs $0.74 for DeepSeek V3.1 Terminus, saving $0.12 (16.1% lower).
Qwen2.5 VL 72B Instruct is $0.12 cheaper on the standard workload (16.1% lower).
Qwen2.5 VL 72B Instruct is $0.02 cheaper per 1M input tokens (7.4% lower; 1.08x difference).
Qwen2.5 VL 72B Instruct is $0.2 cheaper per 1M output tokens (21.1% lower; 1.27x difference).
DeepSeek V3.1 Terminus has 32.77K more context (1.25x larger).

Head-to-Head Specs

Feature	DeepSeek V3.1 Terminus (DeepSeek)	Qwen2.5 VL 72B Instruct (Qwen)
Input Price prompt tokens per 1M	$0.27	$0.25
Completion Price per 1M tokens	$0.95	$0.75
Sample Workload Cost 1M input + 500K output	$0.74	$0.62
Context Window	163.84K	131.07K
Release Date	2025-09-22	2025-02-01
Popularity	#91	#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 $0.74 for DeepSeek V3.1 Terminus, saving $0.12 (16.1% lower).
High-volume input processing	Qwen2.5 VL 72B Instruct	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	DeepSeek V3.1 Terminus	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

DeepSeek V4 Flash (free) can replace DeepSeek V3.1 Terminus when lower sample workload cost matters most: $0.
DeepSeek V4 Flash can replace DeepSeek V3.1 Terminus when lower sample workload cost matters most: $0.2.
R1 Distill Qwen 32B can replace DeepSeek V3.1 Terminus when lower sample workload cost matters most: $0.43.
DeepSeek V3.2 can replace DeepSeek V3.1 Terminus when lower sample workload cost matters most: $0.44.

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

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Larger context alternatives

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Provider catalogs

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DeepSeek catalog

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Qwen catalog

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DeepSeek V3.1 Terminus

DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...

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.