API cost decision in 10 seconds

NewClaude Opus 4.7 (Fast) vs Qwen3.5 397B A17B

Pick Qwen3.5 397B A17B for lower cost; pick Claude Opus 4.7 (Fast) only if the larger context window matters more.

Page updated: 2026-05-26T10:03:26Z Data confirmed: 2026-05-26T12:43:06Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3.5 397B A17B for lower cost; pick Claude Opus 4.7 (Fast) only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Qwen3.5 397B A17B is estimated at $1.56 vs $105 for Claude Opus 4.7 (Fast), saving $103.44 (98.5% lower).

Cost-first pickQwen3.5 397B A17B

Context-first pickClaude Opus 4.7 (Fast)

Sample savings$103.4498.5%

10x traffic gap$1034.4

Claude Opus 4.7 (Fast) has more context, but Qwen3.5 397B A17B saves $103.44 on the standard workload. At 10x that traffic, the same price gap is about $1034.4. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen3.5 397B A17B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Claude Opus 4.7 (Fast)	Qwen3.5 397B A17B
Input-heavy / RAG	5M input + 500K output	Qwen3.5 397B A17B	$225	$3.12
Balanced workload	1M input + 1M output	Qwen3.5 397B A17B	$180	$2.73
Output-heavy chatbot	1M input + 5M output	Qwen3.5 397B A17B	$780	$12.09

Cheaper input Qwen3.5 397B A17B $30 vs $0.39 / 1M

Qwen3.5 397B A17B is $29.61 cheaper per 1M input tokens (98.7% lower; 76.9x difference).

Cheaper output Qwen3.5 397B A17B $150 vs $2.34 / 1M

Qwen3.5 397B A17B is $147.66 cheaper per 1M output tokens (98.4% lower; 64.1x difference).

Larger context Claude Opus 4.7 (Fast) 1M vs 262.14K

Claude Opus 4.7 (Fast) has 737.86K more context (3.81x larger).

Sample workload Qwen3.5 397B A17B $105 vs $1.56

Qwen3.5 397B A17B is $103.44 cheaper on the standard workload (98.5% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Claude Opus 4.7 (Fast) Calculating… Estimated API cost

Qwen3.5 397B A17B 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

Qwen3.5 397B A17B has the lower input price; Qwen3.5 397B A17B has the lower output price; Claude Opus 4.7 (Fast) offers the larger context window. For the 1M input plus 500K output sample, Qwen3.5 397B A17B is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $105 for Claude Opus 4.7 (Fast) and $1.56 for Qwen3.5 397B A17B.

Best Fit

Choose Claude Opus 4.7 (Fast) when you care most about larger context window.

Choose Qwen3.5 397B A17B 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, Qwen3.5 397B A17B is estimated at $1.56 vs $105 for Claude Opus 4.7 (Fast), saving $103.44 (98.5% lower).
Qwen3.5 397B A17B is $103.44 cheaper on the standard workload (98.5% lower).
Qwen3.5 397B A17B is $29.61 cheaper per 1M input tokens (98.7% lower; 76.9x difference).
Qwen3.5 397B A17B is $147.66 cheaper per 1M output tokens (98.4% lower; 64.1x difference).
Claude Opus 4.7 (Fast) has 737.86K more context (3.81x larger).

Head-to-Head Specs

Feature	NewClaude Opus 4.7 (Fast) (Anthropic)	Qwen3.5 397B A17B (Qwen)
Input Price prompt tokens per 1M	$30	$0.39
Completion Price per 1M tokens	$150	$2.34
Sample Workload Cost 1M input + 500K output	$105	$1.56
Context Window	1M	262.14K
Release Date	2026-05-12	2026-02-16

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Qwen3.5 397B A17B	On the standard 1M input plus 500K output workload, Qwen3.5 397B A17B is estimated at $1.56 vs $105 for Claude Opus 4.7 (Fast), saving $103.44 (98.5% lower).
High-volume input processing	Qwen3.5 397B A17B	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	Claude Opus 4.7 (Fast)	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Claude 3 Haiku can replace Claude Opus 4.7 (Fast) when lower sample workload cost matters most: $0.88.
Claude 3.5 Haiku can replace Claude Opus 4.7 (Fast) when lower sample workload cost matters most: $2.8.
Anthropic Claude Haiku Latest can replace Claude Opus 4.7 (Fast) when lower sample workload cost matters most: $3.5.
Claude Haiku 4.5 can replace Claude Opus 4.7 (Fast) when lower sample workload cost matters most: $3.5.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Grok 4.20 Multi-Agent offers 2M context with $5 sample workload cost.
Grok 4.20 offers 2M context with $2.5 sample workload cost.
GPT-5.5 offers 1.05M context with $20 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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Anthropic catalog

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

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Qwen catalog

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

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Claude Opus 4.7 (Fast)

Fast-mode variant of [Opus 4.7](/anthropic/claude-opus-4.7) - identical capabilities with higher output speed at premium 6x pricing. Learn more in Anthropic's docs: https://platform.claude.com/docs/en/build-with-claude/fast-mode

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...