API cost decision in 10 seconds

NewPerceptron Mk1.5 vs Qwen3.8 Omni Flash

Pick Qwen3.8 Omni Flash when budget and context both matter.

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

Budget verdict

Pick Qwen3.8 Omni Flash when budget and context both matter.

On the standard 1M input plus 500K output workload, Qwen3.8 Omni Flash is estimated at $0.39 vs $0.9 for Perceptron Mk1.5, saving $0.52 (57.2% lower).

Cost-first pickQwen3.8 Omni Flash
Context-first pickQwen3.8 Omni Flash
Sample savings$0.5257.2%
10x traffic gap$5.15

Qwen3.8 Omni Flash is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $5.15. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen3.8 Omni Flash stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickPerceptron Mk1.5Qwen3.8 Omni Flash
Input-heavy / RAG5M input + 500K outputQwen3.8 Omni Flash$1.5$0.98
Balanced workload1M input + 1M outputQwen3.8 Omni Flash$1.65$0.62
Output-heavy chatbot1M input + 5M outputQwen3.8 Omni Flash$7.65$2.5
Cheaper input Tie $0.15 vs $0.15 / 1M

Both models report the same input price at $0.15 per 1M tokens.

Cheaper output Qwen3.8 Omni Flash $1.5 vs $0.47 / 1M

Qwen3.8 Omni Flash is $1.03 cheaper per 1M output tokens (68.7% lower; 3.19x difference).

Larger context Qwen3.8 Omni Flash 36.86K vs 1M

Qwen3.8 Omni Flash has 963.14K more context (27.1x larger).

Sample workload Qwen3.8 Omni Flash $0.9 vs $0.39

Qwen3.8 Omni Flash is $0.52 cheaper on the standard workload (57.2% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Perceptron Mk1.5 Calculating… Estimated API cost
Qwen3.8 Omni Flash 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; Qwen3.8 Omni Flash has the lower output price; Qwen3.8 Omni Flash offers the larger context window. For the 1M input plus 500K output sample, Qwen3.8 Omni Flash is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.9 for Perceptron Mk1.5 and $0.39 for Qwen3.8 Omni Flash.

Best Fit

Choose Perceptron Mk1.5 when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Choose Qwen3.8 Omni Flash when you care most about lower output-token price, and larger context window.

Decision Notes
  • On the standard 1M input plus 500K output workload, Qwen3.8 Omni Flash is estimated at $0.39 vs $0.9 for Perceptron Mk1.5, saving $0.52 (57.2% lower).
  • Qwen3.8 Omni Flash is $0.52 cheaper on the standard workload (57.2% lower).
  • Both models report the same input price at $0.15 per 1M tokens.
  • Qwen3.8 Omni Flash is $1.03 cheaper per 1M output tokens (68.7% lower; 3.19x difference).
  • Qwen3.8 Omni Flash has 963.14K more context (27.1x larger).
Head-to-Head Specs
FeatureNewPerceptron Mk1.5
(Perceptron)
Qwen3.8 Omni Flash
(Qwen)
Input Price
prompt tokens per 1M
$0.15$0.15
Completion Price
per 1M tokens
$1.5$0.47
Sample Workload Cost
1M input + 500K output
$0.9$0.39
Context Window36.86K1M
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionQwen3.8 Omni FlashOn the standard 1M input plus 500K output workload, Qwen3.8 Omni Flash is estimated at $0.39 vs $0.9 for Perceptron Mk1.5, saving $0.52 (57.2% lower).
High-volume input processingTieLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsQwen3.8 Omni FlashLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workQwen3.8 Omni FlashA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Cheaper alternatives

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

Open cheapest models

Larger context alternatives

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

Open largest context models

Provider catalogs

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

Open provider hubs

Perceptron catalog

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

Open Perceptron models

Qwen catalog

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

Open Qwen models
Perceptron Mk1.5

Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks,...

Qwen3.8 Omni Flash

Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first Qwen model built around agentic capabilities with native audio-video understanding. It is suited for audio-video analysis and summarization,...