DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next: DashScope Flash-Tier Head-to-Head for Routing Operators

Two flash-tier models are now live on DashScope through the same OpenAI-compatible endpoint, both offering million-token context windows with native multimodal input. DeepSeek V4.1 Flash landed on September 10, 2026. Qwen3.8-Flash-Next arrived on August 26, 2026. They run from the same base_url, bill against the same Aliyun account, and accept the same Authorization header. The only thing an operator needs to change is the model field — and choosing the right value is what this comparison is about.

We looked at architecture, pricing on both the DeepSeek direct API and DashScope, benchmark results from independent evaluators, and practical routing scenarios to help you decide when to pick one, the other, or both.

OpenAI-compatible means a provider exposes a chat-completions endpoint whose request and response shape matches the OpenAI API contract closely enough that an unmodified OpenAI SDK call works against it after swapping three values: API key, base URL, and model name. The minimum surface in practice is POST /v1/chat/completions with messages, model, and an OpenAI-shaped streaming response.

TL;DR Comparison Table

The short version: DeepSeek V4.1 Flash is cheaper per output token, has a documented 384K max output, carries the most aggressive KV cache compression in the flash tier, and leads on agentic coding benchmarks. Qwen3.8-Flash-Next handles more input modalities (audio, video alongside images), costs slightly less per input token on DashScope, and is competitive on coding and general reasoning tasks with a much smaller parameter count.

Architecture Deep Dive

DeepSeek V4.1 Flash

V4.1 Flash introduces a Causal Encoder-Decoder (CED) architecture. The model's 40 Transformer layers split into two halves: a 20-layer causal encoder that processes input, and a 20-layer decoder that generates output. The decoder's global KV cache is projected once from the encoder's final hidden states, then reused across all decoder layers. This is why the model activates only 8B parameters during prefill (processing the prompt) and 16B during decode (generating tokens).

On top of this, Compressed Sparse Attention 2 (CSA2) assigns each attention layer one of three static modes — Full, Reindex, or Reuse — to share KV data and sparse-attention indices across layers rather than recomputing them independently. The result is a KV cache footprint of roughly 890 bytes per token, which DeepSeek reports as 4× smaller than V4 Flash and 437× smaller than DeepSeek V1.

FP4 KV caching in E2M1 format with per-16-channel scaling factors sits on top of CSA2 to compress further without a separate quantization step.

The model also includes Engram conditional memory (196B parameters, sparsely accessed via token-based lookup), DSpark speculative decoding, and a vision encoder (DeepSeek-ViT) trained from scratch with 2D rotary position embeddings.

Qwen3.8-Flash-Next

Qwen3.8-Flash-Next takes a different approach. It is a 125B-parameter MoE model with 6B parameters activated per token, plus a 51B vision encoder — bringing the total to 176B with multimodal components. The architecture introduces Hybrid Attention with Query-Shared Attention (QSA) and Gated DeltaNet, combining standard Transformer attention with linear attention variants to reduce memory consumption during long-context inference.

The model supports text, image, audio, and video inputs — a broader multimodal surface than V4.1 Flash, which handles text and images only.

At roughly 1/6 the total parameter count of V4.1 Flash, Qwen3.8-Flash-Next trades raw model scale for architectural efficiency in the attention mechanism. Where V4.1 Flash pushes KV cache compression through encoder-decoder splitting and CSA2, Flash-Next pushes it through hybrid attention patterns that avoid storing full KV states for every layer.

Pricing Breakdown

Both models are available on DashScope through the same OpenAI-compatible endpoint. V4.1 Flash is also available directly through the DeepSeek API with peak/off-peak pricing.

DeepSeek Direct API (V4.1 Flash only)

Peak hours are 01:00–04:00 UTC and 06:00–10:00 UTC, Monday through Friday. Off-peak covers everything else — weekends included. For workloads that can shift to off-peak windows, the DeepSeek direct API delivers the lowest flash-tier pricing available from any major provider.

The cache hit rate matters enormously here. At $0.003/1M, a cache-heavy agent workload (where long system prompts and tool outputs repeat across turns) can run at a fraction of the headline input price.

DashScope (Both Models)

On DashScope, V4.1 Flash is cheaper on output by a significant margin: ¥2 versus ¥3.4 per million tokens, a 41% gap. Input pricing is nearly identical. For output-heavy workloads — code generation, long-form answers, agent reasoning chains — V4.1 Flash has a clear cost advantage even within the DashScope ecosystem.

Qwen3.8-Flash-Next has a lower implicit cache hit price (¥0.11 versus ¥0.2), so for workloads that heavily reuse context prefixes, the per-request cost gap narrows. But the output price difference typically dominates total cost.

Cost Per Request Example

For a typical request with 10,000 input tokens and 2,000 output tokens on DashScope:

Benchmark Comparison

Independent evaluators have started comparing both models directly. According to llm-stats.com's head-to-head analysis (retrieved September 15, 2026), in the 5 individual benchmarks reported for both models, DeepSeek V4.1 Flash wins 3 and Qwen3.8-Flash-Next wins 2.

V4.1 Flash also reports a Codeforces rating of 3471 (the highest among models compared in its tech report), Terminal-Bench 2.1 at 90.6, and CyberGym at 88.1. These numbers are vendor-reported and have not been independently replicated on identical evaluation suites.

Qwen3.8-Flash-Next achieves "comparable capability against Qwen3.7-Plus" according to Alibaba, with particular strength in coding and agent cooperation tasks — while activating only 6B parameters per token versus Qwen3.7-Plus's much larger footprint.

The pattern from available data: V4.1 Flash leads on agentic coding and tool-use tasks; Flash-Next is competitive on reasoning and general knowledge tasks, especially impressive given its 6× smaller active parameter count.

Multimodal Capabilities

Both models handle text and image input. The difference is in scope.

DeepSeek V4.1 Flash supports text and image inputs through its native DeepSeek-ViT vision encoder, trained from scratch alongside the language model. No audio or video input is supported.

Qwen3.8-Flash-Next supports text, image, audio, and video inputs. This broader multimodal surface makes it the only flash-tier option on DashScope for workloads that need to process audio clips or video frames alongside text.

If your workload involves only text and occasional images, both models are equally capable. If you need audio transcription, video frame analysis, or multi-modal reasoning that combines all four modalities, Flash-Next is the only choice in this tier.

Context Window and Output Limits

Both models support 1M token context windows. The practical difference is in output limits.

V4.1 Flash documents a 384K maximum output — the highest published output limit among flash-tier models, and large enough to generate entire codebases or long-form documents in a single request.

Qwen3.8-Flash-Next has not publicly specified its maximum output token count. In practice, DashScope's default max_tokens applies unless overridden, and the effective limit may be lower than V4.1 Flash's documented ceiling.

For workloads that need very long outputs (full file generation, comprehensive analysis reports, multi-file code generation), V4.1 Flash's documented 384K output ceiling provides a concrete guarantee that Flash-Next does not publicly match.

Speed and Throughput

V4.1 Flash's asymmetric activation (8B input / 16B output) translates directly to inference speed advantages during the prefill phase. Processing a long prompt activates half the parameters compared to decode, which means faster time-to-first-token for prompt-heavy workloads.

DeepSeek sets the concurrency limit at 2,500 RPM on their direct API — 5× higher than the 500 RPM limit on V4 Pro. DashScope's rate limits for both models follow Aliyun's tier-based system and are not publicly documented per model.

Flash-Next's 6B active parameters per token should theoretically deliver fast inference as well, given the extremely small activation footprint. However, Alibaba has not published throughput benchmarks for Flash-Next in tokens-per-second terms, making a direct speed comparison difficult.

For latency-sensitive applications, the DeepSeek direct API with its 2,500 RPM concurrency limit and off-peak pricing offers the most predictable throughput profile.

When to Route to Each Model

Pick DeepSeek V4.1 Flash when:

• Your workload is text-only or text + images and you want the lowest flash-tier output cost
• You need long outputs (up to 384K tokens documented)
• Agentic coding is the primary use case — V4.1 Flash leads on Terminal-Bench, DeepSWE, and CyberGym
• You can schedule workloads during off-peak hours to halve your costs
• KV cache economics matter — the 890 bytes/token footprint means cache-heavy agent loops cost significantly less
• You need the DeepSeek direct API as a fallback path outside DashScope

Pick Qwen3.8-Flash-Next when:

• Your workload needs audio or video input alongside text and images
• Graduate-level reasoning and general knowledge quality matters more than coding performance
• You prefer to run everything through a single DashScope account without an additional DeepSeek API key
• The implicit cache hit pricing (¥0.11/1M) makes it cheaper for your specific prefix-reuse pattern
• You want to stay within the Qwen ecosystem for consistency with other Qwen models in your routing config

Run both behind TheRouter when:

Route to V4.1 Flash as the primary for text/image workloads and coding tasks. Fall back to Flash-Next when the request includes audio or video input, or when DeepSeek's direct API is experiencing elevated latency. Both models sit behind the same DashScope OpenAI-compatible endpoint, so the routing config only needs to swap the model parameter — no base_url change required.

# TheRouter fallback: DashScope flash tier
- provider: dashscope
  model: deepseek-v4-flash        # Routes to V4.1 Flash
  priority: 1
- provider: dashscope
  model: qwen3.8-flash-next       # Fallback for multimodal or DeepSeek issues
  priority: 2

Frequently Asked Questions

Are both models available through the same DashScope API endpoint?

Yes. Both use https://dashscope.aliyuncs.com/compatible-mode/v1 with the same API key. The only difference is the model parameter value.

Can I use the DeepSeek direct API for V4.1 Flash instead of DashScope?

Yes. V4.1 Flash is available at https://api.deepseek.com using the model name deepseek-flash. The direct API has peak/off-peak pricing and a 2,500 RPM concurrency limit. Qwen3.8-Flash-Next is not available through the DeepSeek API.

Which model is better for coding tasks?

V4.1 Flash leads on published agentic coding benchmarks (DeepSWE v1.1, Terminal-Bench 2.1, Codeforces rating 3471). Flash-Next is competitive but trails on these specific evaluations. For pure coding workloads, V4.1 Flash is the stronger choice.

Which model handles more input types?

Qwen3.8-Flash-Next supports text, images, audio, and video. V4.1 Flash supports text and images only. If your pipeline includes audio or video inputs, Flash-Next is the only option in this tier.

How do the KV cache economics compare?

V4.1 Flash reports an 890-byte-per-token KV cache footprint — 4× smaller than V4 Flash. This translates to cheaper cache-hit pricing on the DeepSeek direct API ($0.003/1M off-peak). On DashScope, V4.1 Flash's implicit cache hit is ¥0.2/1M versus Flash-Next's ¥0.11/1M, so Flash-Next actually has a lower cache-hit rate on DashScope despite V4.1 Flash's smaller footprint.

Do both models support function calling and tool use?

Yes. Both support OpenAI-compatible function calling through the DashScope endpoint. V4.1 Flash also supports function calling, JSON output, and the Responses API through the DeepSeek direct API.

Sources

• DeepSeek V4.1 Flash announcement — api-docs.deepseek.com/news/news260910 (retrieved Sep 15, 2026)
• DeepSeek API pricing — api-docs.deepseek.com/quick_start/pricing (retrieved Sep 15, 2026)
• DashScope model pricing — help.aliyun.com/zh/model-studio/model-pricing (retrieved Sep 15, 2026)
• V4.1 Flash architecture specs — mindstudio.ai/blog/deepseek-v4-1-flash-specs-architecture (retrieved Sep 15, 2026)
• llm-stats.com head-to-head comparison — llm-stats.com/models/compare/deepseek-v4.1-flash-vs-qwen3.8-flash-next (retrieved Sep 15, 2026)
• Qwen3.8-Flash-Next announcement — qwen.ai/blog?id=qwen3.8-flash-next (retrieved Sep 15, 2026)
• DashScope DeepSeek V4 Flash model page — help.aliyun.com/en/model-studio/deepseek-v4-flash (retrieved Sep 15, 2026)