DeepSeek-V4.1-Flash vs Qwen3.8-Flash: The Open and Closed Flash Tiers

All figures below are from the China AI Hub database, last verified 2026-09-20 (Qwen3.8-Flash last verified 2026-09-27). Where a field is not publicly disclosed, we say so rather than estimating.

At a glance

Dimension	DeepSeek-V4.1-Flash	Qwen3.8-Flash
Status (database)	active	active
Context window	1,048,576 tokens	1,048,576 tokens
Maximum output	393,216 tokens	131,072 tokens
Input price (per 1M)	$0.15 (off-peak)	$0.15 (Singapore)
Output price (per 1M)	$0.60 (off-peak)	$0.47 (Singapore)
Open weights	Yes (MIT)	No (proprietary)
API available	Yes	Yes
Reasoning	Yes	Yes
Coding	Yes	Not listed
Vision / Video	Yes / Not listed	Yes / Yes
Tool calling	Yes	Not listed
Function calling	Yes	Not listed
Structured output	Yes	Not listed
Benchmark records in DB	5	0

Pricing

Both list $0.15 input per 1M tokens — the flash-tier floor. DeepSeek-V4.1-Flash lists $0.60 output; Qwen3.8-Flash lists $0.47 output (Singapore region; Beijing/Global list $0.113/$0.382). DeepSeek additionally lists half-price off-peak rates with a 2x peak window and cache reads at $0.003; Qwen’s region spread is the analogous variable. At this tier the per-token differences are small; the openness and capability differences matter more.

Context and output

Identical 1,048,576-token context windows. DeepSeek-V4.1-Flash lists a 393,216-token maximum output versus Qwen3.8-Flash’s 131,072 — a 3x difference for long generations.

Architecture and parameters

DeepSeek-V4.1-Flash is a 552B-parameter MoE that activates only 8B parameters on input and 16B on output — the most aggressively sparse model in the database, described by DeepSeek as a Causal Encoder-Decoder MoE claiming 1/4 the HBM and 1/8 the SSD KV-cache storage of the previous generation. Qwen3.8-Flash does not disclose architecture or parameter counts. The architecture disclosure is itself a recorded difference: DeepSeek publishes a full sparsity card for the flash tier, while Alibaba documents the Qwen3.8-Flash only by capability and price.

Coding

This is the clearest split. DeepSeek-V4.1-Flash lists coding and publishes vendor-reported Terminal-Bench 2.1 (90.6), Codeforces (3471) and DeepSWE v1.1 (74.2). Qwen3.8-Flash does not list coding in its recorded capability set (reasoning, vision and video only) and has no benchmark records. For coding workloads the database records a clear difference in listed capability.

Vision

Both list vision. Qwen3.8-Flash additionally lists video input; DeepSeek-V4.1-Flash lists vision without a video flag. For image input both apply; video input is the recorded difference.

Agent capabilities

DeepSeek-V4.1-Flash lists tool calling, function calling and structured output — the surface for structured, tool-driven agent pipelines. Qwen3.8-Flash lists none of these in its recorded set. DeepSeek positions V4.1-Flash as the agentic-coding workhorse of its current lineup, and its native vision plus tool calling make it usable in multimodal agent loops; Qwen3.8-Flash has no corresponding documented agent surface. For structured-output and tool-driven workflows, only DeepSeek lists the relevant capabilities.

License and openness

This is the largest structural split. DeepSeek-V4.1-Flash is MIT open weight and self-hostable; Qwen3.8-Flash is proprietary, closed weight, no self-hosting. Organizations with self-hosting or data-residency requirements have an option in one and not the other.

Deployment

DeepSeek-V4.1-Flash is served through the DeepSeek Platform and is open weight for self-hosting. Qwen3.8-Flash is served through Alibaba Cloud Model Studio across six regions (Beijing, Singapore, Hong Kong, Frankfurt, US-Virginia, Tokyo), API-only, with batch inference not supported. The deployment trade is self-hosting (DeepSeek) versus a broad multi-region API footprint (Qwen).

Benchmarks

The database holds 5 benchmark records for DeepSeek-V4.1-Flash — GPQA Diamond (90.9), HLE (36.8), Codeforces (3471), Terminal-Bench 2.1 (90.6) and DeepSWE v1.1 (74.2) — and 0 for Qwen3.8-Flash. All DeepSeek rows are vendor-reported (via the DeepSeek Harness).

Benchmark comparability is limited: Qwen3.8-Flash has no benchmark records, so no head-to-head score comparison is possible; DeepSeek’s scores are vendor-reported under a specific harness.

Why each difference matters

China AI Hub analysis indicates the following per-dimension implications, drawn from the listed facts above.

• Pricing: both list $0.15 input, so price is a weak discriminator; Qwen3.8-Flash lists a slightly lower output price, and DeepSeek-V4.1-Flash has an advantage in off-peak scheduling with half-price off-peak rates.
• Context: identical 1M-token input windows; DeepSeek-V4.1-Flash has an advantage in long single generations with a 3x larger maximum output (393,216 vs 131,072).
• Coding: DeepSeek-V4.1-Flash is more relevant for coding workloads — it lists coding and coding benchmarks where Qwen3.8-Flash lists neither.
• Vision / media: both list vision; Qwen3.8-Flash additionally lists video input.
• Agent: DeepSeek-V4.1-Flash is more relevant for structured, tool-driven pipelines — it lists tool calling, function calling and structured output where Qwen3.8-Flash lists none.
• License: DeepSeek-V4.1-Flash has an advantage for self-hosting (MIT open weight); Qwen3.8-Flash has a limitation here (proprietary, API-only).
• Deployment: DeepSeek offers self-hosting; Qwen offers a broader six-region API footprint — choose by which constraint binds.
• Benchmark: DeepSeek records 5 and Qwen records 0; the evidence base is not comparable.

Trade-off summary

• Openness: DeepSeek-V4.1-Flash is MIT open weight; Qwen3.8-Flash is API-only proprietary.
• Coding and tools: DeepSeek-V4.1-Flash lists coding, tool calling and structured output; Qwen3.8-Flash lists none.
• Output length: DeepSeek lists a 3x larger maximum output.
• Video: Qwen3.8-Flash lists video input; DeepSeek lists vision without video.
• Price: essentially tied at $0.15 input.

Choose by openness and workload: self-hosting, coding, tool-driven and long-generation work favors DeepSeek-V4.1-Flash’s listed capabilities and MIT license; video input and a broad multi-region API footprint favor Qwen3.8-Flash. Verify current prices on the official pages before committing.

Decision context

China AI Hub analysis indicates the following decision-context implications, drawn from the listed facts above.

For API developers. Both list $0.15 input, so cost is a weak discriminator. DeepSeek-V4.1-Flash lists coding, tool calling, function calling and structured output, plus a 393K output ceiling and half-price off-peak rates; Qwen3.8-Flash lists vision/video but none of the coding/tool surface. Latency is not publicly documented on this page.

For self-hosting. This is the clearest split: DeepSeek-V4.1-Flash is MIT open weight and self-hostable; Qwen3.8-Flash is proprietary and API-only. Hardware requirements and quantization are Not publicly documented on this page.

For coding agents. DeepSeek-V4.1-Flash lists coding, tool calling and structured output plus coding benchmarks (Terminal-Bench 2.1 90.6, DeepSWE v1.1 74.2), which is more relevant for coding-agent pipelines; Qwen3.8-Flash lists none of these.

For enterprise. Deployment differs: DeepSeek offers self-hosting (data-residency option), while Qwen offers a six-region API footprint. Region, SLA and compliance terms are not publicly documented — confirm with the vendor.

What is uncertain

• Qwen3.8-Flash has no benchmark records and no documented coding/tool capability — an absence of documentation, not a verified lack of capability.
• Qwen3.8-Flash’s architecture and parameter counts are not publicly disclosed.
• DeepSeek’s benchmark scores are vendor-reported via a specific harness and not independently verified.

Sources

• DeepSeek API docs — Models & Pricing
• DeepSeek V4.1-Flash announcement
• Hugging Face model card — DeepSeek-V4.1-Flash
• Alibaba Cloud Model Studio — Qwen3.8-Flash
• Alibaba Cloud Model Studio — Model pricing

Labels used above: Official fact (prices, context windows, capabilities and license terms from primary provider sources), Vendor-reported claim (benchmark scores), and China AI Hub analysis (the “why each difference matters” reasoning, introduced as analysis). No third-party benchmark evidence is currently recorded for these models.