API cost decision in 10 seconds

Step 3.5 Flash vs Qwen3 Next 80B A3B Instruct

Pick Step 3.5 Flash when budget is the priority.

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 Step 3.5 Flash when budget is the priority.

On the standard 1M input plus 500K output workload, Step 3.5 Flash is estimated at $0.24 vs $0.64 for Qwen3 Next 80B A3B Instruct, saving $0.4 (62.5% lower).

Cost-first pickStep 3.5 Flash

Context-first pickBoth models

Sample savings$0.462.5%

10x traffic gap$4

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $4. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Step 3.5 Flash stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Step 3.5 Flash	Qwen3 Next 80B A3B Instruct
Input-heavy / RAG	5M input + 500K output	Step 3.5 Flash	$0.6	$1
Balanced workload	1M input + 1M output	Step 3.5 Flash	$0.39	$1.19
Output-heavy chatbot	1M input + 5M output	Step 3.5 Flash	$1.59	$5.59

Cheaper input Tie $0.09 vs $0.09 / 1M

Both models report the same input price at $0.09 per 1M tokens.

Cheaper output Step 3.5 Flash $0.3 vs $1.1 / 1M

Step 3.5 Flash is $0.8 cheaper per 1M output tokens (72.7% lower; 3.67x difference).

Larger context Tie 262.14K vs 262.14K

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

Sample workload Step 3.5 Flash $0.24 vs $0.64

Step 3.5 Flash is $0.4 cheaper on the standard workload (62.5% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Step 3.5 Flash Calculating… Estimated API cost

Qwen3 Next 80B A3B 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

both models tie on input price; Step 3.5 Flash has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Step 3.5 Flash is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.24 for Step 3.5 Flash and $0.64 for Qwen3 Next 80B A3B Instruct.

Best Fit

Choose Step 3.5 Flash when you care most about lower output-token price.

Choose Qwen3 Next 80B A3B Instruct when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Decision Notes

On the standard 1M input plus 500K output workload, Step 3.5 Flash is estimated at $0.24 vs $0.64 for Qwen3 Next 80B A3B Instruct, saving $0.4 (62.5% lower).
Step 3.5 Flash is $0.4 cheaper on the standard workload (62.5% lower).
Both models report the same input price at $0.09 per 1M tokens.
Step 3.5 Flash is $0.8 cheaper per 1M output tokens (72.7% lower; 3.67x difference).
Both models report the same context window at 262.14K tokens.

Head-to-Head Specs

Feature	Step 3.5 Flash (StepFun)	Qwen3 Next 80B A3B Instruct (Qwen)
Input Price prompt tokens per 1M	$0.09	$0.09
Completion Price per 1M tokens	$0.3	$1.1
Sample Workload Cost 1M input + 500K output	$0.24	$0.64
Context Window	262.14K	262.14K
Release Date	2026-01-29	2025-09-11
Popularity	#43	#79

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Step 3.5 Flash	On the standard 1M input plus 500K output workload, Step 3.5 Flash is estimated at $0.24 vs $0.64 for Qwen3 Next 80B A3B Instruct, saving $0.4 (62.5% lower).
High-volume input processing	Tie	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Step 3.5 Flash	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Tie	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 Next 80B A3B Instruct when lower sample workload cost matters most: $0.
Qwen3 Coder 480B A35B (free) can replace Qwen3 Next 80B A3B Instruct when lower sample workload cost matters most: $0.
Qwen2.5 7B Instruct can replace Qwen3 Next 80B A3B Instruct when lower sample workload cost matters most: $0.09.
Qwen3.5-9B can replace Qwen3 Next 80B A3B Instruct 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.
Gemini 2.5 Flash Lite offers 1.05M context with $0.3 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

Find models with larger context windows for RAG, long documents, and codebase review.

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Provider catalogs

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

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StepFun catalog

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

Open StepFun models

Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

Open Qwen models

Step 3.5 Flash

Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....

Qwen3 Next 80B A3B Instruct

Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual...