API cost decision in 10 seconds

GPT-5.4 Mini vs Qwen3 235B A22B Instruct 2507

Pick Qwen3 235B A22B Instruct 2507 for lower cost; pick GPT-5.4 Mini only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3 235B A22B Instruct 2507 for lower cost; pick GPT-5.4 Mini only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Qwen3 235B A22B Instruct 2507 is estimated at $0.12 vs $3 for GPT-5.4 Mini, saving $2.88 (96% lower).

Cost-first pickQwen3 235B A22B Instruct 2507
Context-first pickGPT-5.4 Mini
Sample savings$2.8896%
10x traffic gap$28.79

GPT-5.4 Mini has more context, but Qwen3 235B A22B Instruct 2507 saves $2.88 on the standard workload. At 10x that traffic, the same price gap is about $28.79. 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 Instruct 2507 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickGPT-5.4 MiniQwen3 235B A22B Instruct 2507
Input-heavy / RAG5M input + 500K outputQwen3 235B A22B Instruct 2507$6$0.4
Balanced workload1M input + 1M outputQwen3 235B A22B Instruct 2507$5.25$0.17
Output-heavy chatbot1M input + 5M outputQwen3 235B A22B Instruct 2507$23.25$0.57
Cheaper input Qwen3 235B A22B Instruct 2507 $0.75 vs $0.071 / 1M

Qwen3 235B A22B Instruct 2507 is $0.68 cheaper per 1M input tokens (90.5% lower; 10.6x difference).

Cheaper output Qwen3 235B A22B Instruct 2507 $4.5 vs $0.1 / 1M

Qwen3 235B A22B Instruct 2507 is $4.4 cheaper per 1M output tokens (97.8% lower; 45x difference).

Larger context GPT-5.4 Mini 400K vs 262.14K

GPT-5.4 Mini has 137.86K more context (1.53x larger).

Sample workload Qwen3 235B A22B Instruct 2507 $3 vs $0.12

Qwen3 235B A22B Instruct 2507 is $2.88 cheaper on the standard workload (96% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
GPT-5.4 Mini Calculating… Estimated API cost
Qwen3 235B A22B Instruct 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 Instruct 2507 has the lower input price; Qwen3 235B A22B Instruct 2507 has the lower output price; GPT-5.4 Mini offers the larger context window. For the 1M input plus 500K output sample, Qwen3 235B A22B Instruct 2507 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $3 for GPT-5.4 Mini and $0.12 for Qwen3 235B A22B Instruct 2507.

Best Fit

Choose GPT-5.4 Mini when you care most about larger context window.

Choose Qwen3 235B A22B Instruct 2507 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 235B A22B Instruct 2507 is estimated at $0.12 vs $3 for GPT-5.4 Mini, saving $2.88 (96% lower).
  • Qwen3 235B A22B Instruct 2507 is $2.88 cheaper on the standard workload (96% lower).
  • Qwen3 235B A22B Instruct 2507 is $0.68 cheaper per 1M input tokens (90.5% lower; 10.6x difference).
  • Qwen3 235B A22B Instruct 2507 is $4.4 cheaper per 1M output tokens (97.8% lower; 45x difference).
  • GPT-5.4 Mini has 137.86K more context (1.53x larger).
Head-to-Head Specs
FeatureGPT-5.4 Mini
(OpenAI)
Qwen3 235B A22B Instruct 2507
(Qwen)
Input Price
prompt tokens per 1M
$0.75$0.071
Completion Price
per 1M tokens
$4.5$0.1
Sample Workload Cost
1M input + 500K output
$3$0.12
Context Window400K262.14K
Release Date
Popularity#31#42

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionQwen3 235B A22B Instruct 2507On the standard 1M input plus 500K output workload, Qwen3 235B A22B Instruct 2507 is estimated at $0.12 vs $3 for GPT-5.4 Mini, saving $2.88 (96% lower).
High-volume input processingQwen3 235B A22B Instruct 2507Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsQwen3 235B A22B Instruct 2507Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGPT-5.4 MiniA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • gpt-oss-120b (free) can replace GPT-5.4 Mini when lower sample workload cost matters most: $0.
  • gpt-oss-20b (free) can replace GPT-5.4 Mini when lower sample workload cost matters most: $0.
  • gpt-oss-20b can replace GPT-5.4 Mini when lower sample workload cost matters most: $0.1.
  • gpt-oss-120b can replace GPT-5.4 Mini when lower sample workload cost matters most: $0.13.
Larger context near this budget
  • Llama 4 Scout offers 10M context with $0.23 sample workload cost.
  • Grok 4.20 offers 2M context with $2.5 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.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

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Larger context alternatives

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

Open largest context models

Provider catalogs

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OpenAI catalog

Review all tracked OpenAI 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.

Open Qwen models
GPT-5.4 Mini

GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...

Qwen3 235B A22B Instruct 2507

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...