Phi-3.5-mini-instruct vs Qwen3.8 Flash
Qwen3.8 Flash leads the LLM Stats Score 49.6 to -3.7. Phi-3.5-mini-instruct is 2.3x cheaper per token.
Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8 Flash leads the overall LLM Stats Score 49.6 to -3.7, ranking #16 overall.
In the 1 individual benchmarks reported for both models, Qwen3.8 Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Phi-3.5-mini-instruct is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3.8 Flash also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Phi-3.5-mini-instruct
- cost matters — it's about 2.3x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Qwen3.8 Flash
- overall performance matters — it scores 49.6 and ranks #16 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
31 reported for Phi-3.5-mini-instruct · 22 for Qwen3.8 Flash
Phi-3.5-mini-instruct outperforms in 0 benchmarks, while Qwen3.8 Flash is better at 1 benchmark (GPQA).
Qwen3.8 Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Phi-3.5-mini-instruct ($0.10/1M tokens) is 1.5x cheaper than Qwen3.8 Flash ($0.15/1M tokens).
For output processing, Phi-3.5-mini-instruct ($0.10/1M tokens) is 4.7x cheaper than Qwen3.8 Flash ($0.47/1M tokens).
In conclusion, Qwen3.8 Flash is more expensive than Phi-3.5-mini-instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.8 Flash has 121.2B more parameters than Phi-3.5-mini-instruct, making it 3189.5% larger.
Context Window
Maximum input and output token capacity
Qwen3.8 Flash accepts 1,000,000 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Qwen3.8 Flash can generate longer responses up to 131,072 tokens, while Phi-3.5-mini-instruct is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8 Flash supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.
Qwen3.8 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
Phi-3.5-mini-instruct
Qwen3.8 Flash
License
Usage and distribution terms
Phi-3.5-mini-instruct is licensed under MIT, while Qwen3.8 Flash uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Phi-3.5-mini-instruct was released on 2024-08-23, while Qwen3.8 Flash was released on 2026-08-26.
Qwen3.8 Flash is 24 months newer than Phi-3.5-mini-instruct.
Aug 23, 2024
2.0 years ago
Aug 26, 2026
5 days ago
2.0yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Phi-3.5-mini-instruct is available from Azure. Qwen3.8 Flash is available from Novita.
Phi-3.5-mini-instruct
Qwen3.8 Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against Phi-3.5-mini-instruct and Qwen3.8 Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about Phi-3.5-mini-instruct vs Qwen3.8 Flash.