API cost decision in 10 seconds

R1 0528 vs Qwen3 235B A22B Thinking 2507

Pick Qwen3 235B A22B Thinking 2507 when budget and context both matter.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T19:08:13Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3 235B A22B Thinking 2507 when budget and context both matter.

On the standard 1M input plus 500K output workload, Qwen3 235B A22B Thinking 2507 is estimated at $0.9 vs $1.57 for R1 0528, saving $0.68 (43% lower).

Cost-first pickQwen3 235B A22B Thinking 2507

Context-first pickQwen3 235B A22B Thinking 2507

Sample savings$0.6843%

10x traffic gap$6.78

Qwen3 235B A22B Thinking 2507 is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $6.78. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen3 235B A22B Thinking 2507 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	R1 0528	Qwen3 235B A22B Thinking 2507
Input-heavy / RAG	5M input + 500K output	Qwen3 235B A22B Thinking 2507	$3.58	$1.5
Balanced workload	1M input + 1M output	Qwen3 235B A22B Thinking 2507	$2.65	$1.64
Output-heavy chatbot	1M input + 5M output	Qwen3 235B A22B Thinking 2507	$11.25	$7.62

Cheaper input Qwen3 235B A22B Thinking 2507 $0.5 vs $0.1495 / 1M

Qwen3 235B A22B Thinking 2507 is $0.35 cheaper per 1M input tokens (70.1% lower; 3.34x difference).

Cheaper output Qwen3 235B A22B Thinking 2507 $2.15 vs $1.495 / 1M

Qwen3 235B A22B Thinking 2507 is $0.65 cheaper per 1M output tokens (30.5% lower; 1.44x difference).

Larger context Qwen3 235B A22B Thinking 2507 163.84K vs 262.14K

Qwen3 235B A22B Thinking 2507 has 98.3K more context (1.6x larger).

Sample workload Qwen3 235B A22B Thinking 2507 $1.57 vs $0.9

Qwen3 235B A22B Thinking 2507 is $0.68 cheaper on the standard workload (43% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

R1 0528 Calculating… Estimated API cost

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

For a 1M input token plus 500K output token workload, the estimated API cost is $1.57 for R1 0528 and $0.9 for Qwen3 235B A22B Thinking 2507.

Best Fit

Choose R1 0528 when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

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

Decision Notes

On the standard 1M input plus 500K output workload, Qwen3 235B A22B Thinking 2507 is estimated at $0.9 vs $1.57 for R1 0528, saving $0.68 (43% lower).
Qwen3 235B A22B Thinking 2507 is $0.68 cheaper on the standard workload (43% lower).
Qwen3 235B A22B Thinking 2507 is $0.35 cheaper per 1M input tokens (70.1% lower; 3.34x difference).
Qwen3 235B A22B Thinking 2507 is $0.65 cheaper per 1M output tokens (30.5% lower; 1.44x difference).
Qwen3 235B A22B Thinking 2507 has 98.3K more context (1.6x larger).

Head-to-Head Specs

Feature	R1 0528 (DeepSeek)	Qwen3 235B A22B Thinking 2507 (Qwen)
Input Price prompt tokens per 1M	$0.5	$0.1495
Completion Price per 1M tokens	$2.15	$1.495
Sample Workload Cost 1M input + 500K output	$1.57	$0.9
Context Window	163.84K	262.14K
Release Date	2025-05-28	2025-07-25
Popularity	#106	#133

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Qwen3 235B A22B Thinking 2507	On the standard 1M input plus 500K output workload, Qwen3 235B A22B Thinking 2507 is estimated at $0.9 vs $1.57 for R1 0528, saving $0.68 (43% 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	Qwen3 235B A22B Thinking 2507	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

DeepSeek V4 Flash (free) can replace R1 0528 when lower sample workload cost matters most: $0.
DeepSeek V4 Flash can replace R1 0528 when lower sample workload cost matters most: $0.2.
R1 Distill Qwen 32B can replace R1 0528 when lower sample workload cost matters most: $0.43.
DeepSeek V3.2 can replace R1 0528 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

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Provider catalogs

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DeepSeek catalog

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Qwen catalog

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R1 0528

May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...

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...