DEV Community

Jathurshan Sownthararasa
Jathurshan Sownthararasa

Posted on

DeepSeek V4.1 Flash vs Mistral Large 4 vs Qwen3.8-27B: Which Open Model?

#ai #llm #opensource #deepseek

Open-weight models now span a huge range: from a 27B model that fits on one GPU to a trillion-parameter mixture-of-experts that needs a small cluster. This comparison covers three that matter right now: DeepSeek-V4.1-Flash-1017, Mistral Large 4 and Qwen3.8-27B. All data is as of October 31, 2026.

DeepSeek-V4.1-Flash-1017

DeepSeek's October 17 update to V4.1 Flash is a sparse mixture-of-experts model. Quantizer cards describe about 763B parameters, with 6 of 384 routed experts active per token and a 1M-token context window. It takes image and text input. Downstream cards list the license as MIT, so confirm that on the official card.

Pricing is the headline. Pricing trackers citing the official API (deepseek-flash) report $0.15 per million input tokens and $0.60 per million output tokens off-peak. Peak rates are double, and cache hits are a fraction of a cent. Prices were verified between Oct 23 and Oct 25, 2026.

The weakness is size: the official checkpoint is roughly 510 GB, so self-hosting is a serious job. I also could not verify benchmark numbers for the 1017 build specifically, so I make no performance claims for it.

Model card: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017

Mistral Large 4

Mistral Large 4 is a multimodal MoE with about 1.05T total parameters and 49B active per token (52B counting embeddings). Weights were published on Oct 29, three weeks after an API preview. It accepts text and images and outputs text. It supports configurable reasoning effort, native function calling and strict JSON schema output. Mistral says it is fluent in 160+ languages.

The context window is listed as 1M tokens, but some gateways exposed about 512K–524K, so test your endpoint. Pricing sources conflict. One reports $0.70 input / $2.10 output per million tokens from the Oct 29 announcement, while others cite a list price of $1.36 / $4.18 with a promotional half price. Check Mistral's pricing page before budgeting.

Self-hosting needs roughly eight H200-class GPUs. The license is the Mistral Ecosystem Open-Weight License, and NVIDIA's model card notes that running a model-as-a-service business requires a separate commercial license. Independent benchmark coverage was thin and mixed when I checked, so I avoid ranking claims.

Model card: https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B

Qwen3.8-27B

Qwen3.8-27B is a dense 27B vision-language model. It takes text, images and video, and it has thinking control. Native context is 262,144 tokens, extensible to 1M with YaRN scaling. It is released under Apache 2.0. Unsloth's guide says 4-bit quants run on about 17 GB of memory.

Qwen reports 61.7 on SWE-bench Pro and 73.0 on Terminal-Bench 2.1, up from 53.5 and 63.4 for Qwen3.6-27B. Two caveats apply. These runs use Qwen's own Claude Code harness, and the Opus 4.6 Max SWE-bench Pro figure is Anthropic's published score rather than a re-run. Opus 4.6 Max still leads on Terminal-Bench 2.1. I did not find a list price for Qwen's hosted version, so the cost story here is mostly self-hosting.

Model card: https://huggingface.co/Qwen/Qwen3.8-27B

Verdict: choose X if...

  • Choose DeepSeek V4.1 Flash if you want cheap, long-context API calls at volume and can live with limited verified benchmark data for this specific build.
  • Choose Mistral Large 4 if you want the largest model of the three, with strong multilingual claims, and you can run or pay for multi-GPU infrastructure. Read the license first if you plan to resell access.
  • Choose Qwen3.8-27B if you want a permissively licensed model for local coding agents on one GPU, and 262K native context is enough.

How we picked these

Criteria: the model is recent (released or updated within the last few months), has open weights, covers text plus vision input, and has an official model page. We compared context window, modalities, license, pricing and the benchmark evidence available. Benchmarks used are vendor-reported: Qwen's SWE-bench Pro and Terminal-Bench 2.1 numbers from its model card. They are not like-for-like across vendors. Hugging Face model cards and pricing trackers were checked between Oct 23 and Oct 31, 2026. Prices change often, so confirm on each provider's site.

Sources

Top comments (0)