DeepSeek V4 Flash: Gonka network model with 380K context

DeepSeek is a Chinese AI lab from Hangzhou that gained worldwide fame after the releases of V3 and R1: it was R1 in early 2025 that showed an open model could catch up to closed-source flagships for a fraction of their budget. In April 2026, DeepSeek released the V4 family consisting of two models: the heavy V4 Pro and the lightweight V4 Flash. Both are open (MIT license), and both are built on the MoE architecture.

V4 Flash has 284 billion parameters, of which about 13 billion are activated per token. This sparsity makes the model fast and cheap to maintain: it requires significantly less VRAM than "dense" giants, and the generation speed is higher.

The version running on the Gonka network is V4-Flash-0731 — a re-post-training build from July 31, 2026. The architecture and size have not changed, but the quality on agentic tasks has jumped: according to nine agentic and coding benchmarks published by DeepSeek, the 0731 build even outperforms their own flagship, V4 Pro, in preview version. An important caveat for fairness: some of these figures are measurements by DeepSeek itself on its own test bench, which had not been publicly released at the time of writing, so there is no independent replication yet. The gap with closed-source frontier models remains: the 0731 build does not outperform Claude Opus 4.8 on any of the nine benchmarks, but it costs orders of magnitude less, which is the whole point: maximum agentic quality for a fraction of the price.

As with other network models, it is important to distinguish between "out-of-the-box" model specs and the operational parameters of a specific deployment: these are determined by the vLLM inference configuration on the network's GPU hosts. Actual values via our Gateway:

• Context window: 380,000 tokens — the longest in the Gonka network. We verified this empirically: a request with an input of 381,000 tokens was accepted and processed in tens of seconds. The V4 Flash architecture itself supports a context of up to 1 million tokens; the practical ceiling on the network is determined by host configuration (inference window of 400,000).
• Max output: 8,192 tokens per response — a unified ceiling for all network models (gateway configuration, not a model limit).
• Reasoning and tool calling: the model is deployed with reasoning parsers and tool invocation — agentic scenarios (Cursor, Claude Code, LangChain) work out of the box; we have tested multi-step tool scenarios via OpenAI and Anthropic-compatible paths.
• Host VRAM requirement: about 280 GB — the lightest model on the network (for comparison: MiniMax M2.7 — 320 GB, Kimi K2.6 — 720 GB). The lower the hardware threshold, the easier it is for hosts to deploy the model — a good sign for its availability on the network.

Inference pricing on the Gonka network does not depend on the model choice: DeepSeek V4 Flash is available at the same rate as Kimi K2.6 and MiniMax M2.7 — via JoinGonka Gateway, this is $0.0032 per million input tokens and $0.0097 per million output tokens. For reference: the official DeepSeek API sells the same model for $0.14/$0.28 per million, and OpenRouter for $0.08/$0.18. Network economics is a separate topic: the price is determined by the calculation of computational work, not the vendor's price list.

There are now three active models in the network (a fourth, GLM-5.2, is registered and awaiting deployment). A brief comparison:

A practical consequence of the identical price is that you can choose the model purely based on the task, without worrying about the budget. Need the longest context or fast responses with reasoning? Go for DeepSeek V4 Flash. For maximum agentic reliability on complex code, use Kimi K2.6. For a solid daily workhorse, choose MiniMax M2.7.

Key published results for the 0731 build (according to DeepSeek and independent aggregators):

• SWE-bench Verified: 79.0% — solving real GitHub tasks; a level that only closed-source flagships reached a year ago. For comparison, Kimi K2.6 is at 71.3%.
• GPQA Diamond: 88.1% — graduate-level scientific questions; the advanced reasoning mode adds precision to multi-step tasks.
• Terminal Bench 2.1: 82.7 — agentic terminal operations; the Flash preview version had 61.8, while V4 Pro Preview had 72.1.
• DeepSWE: 54.4 — a new rigorous DeepSeek benchmark with nearly zero false-positive rates; these are vendor-provided figures, the stand has not yet been published—take this with a grain of salt.

A fair framework: across nine agentic benchmarks, the 0731 build outperforms its own V4 Pro Preview but still lags behind Claude Opus 4.8 by about 6 points on average. However, the price per token is two orders of magnitude cheaper than Opus, and via the Gonka network, it is dozens of times cheaper than the official DeepSeek API. This ratio of "frontier-level quality at a fraction of the cost" is what makes it compelling. Detailed independent measurements can be found at Artificial Analysis, and the weights and model card are available on Hugging Face.

The model is available via the JoinGonka Gateway using an OpenAI- and Anthropic-compatible API. Model ID: deepseek-ai/DeepSeek-V4-Flash-0731.

The fastest way is the one-command installer, which configures your tools (Claude Code, OpenClaw, Cline, opencode, Aider, etc.) to use this model immediately:

npx @joingonka/setup --model deepseek

Direct API call (OpenAI format):

curl https://gate.joingonka.ai/v1/chat/completions \
  -H "Authorization: Bearer jg-your-key" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-ai/DeepSeek-V4-Flash-0731","messages":[{"role":"user","content":"Hello!"}]}'

Upon registration, you receive 10,000,000 free tokens—with a 380K context, this is an opportunity to immediately test the model on real large documents and codebases. Step-by-step examples for Python and TypeScript can be found in the API Quickstart. You can also try it without registration in the free chat by selecting DeepSeek V4 Flash from the model list.

Strong scenarios for this model:

• Large documents and entire codebases. 380,000 tokens is about 280,000 words: an annual report, a legal document package, or an average repository can fit into a single request without cutting into pieces or losing connections between parts.
• Long agentic sessions. An autonomous agent that reads files, calls tools, and accumulates history hits the context limit fastest—a buffer of 380K tokens means significantly longer sessions without resetting memory.
• RAG with large samplings. The more relevant documents fit into the context, the less dependence on search accuracy.
• Reasoning tasks. The reasoning mode provides a boost in math, science, and multi-step logic—at the price of a standard model.

When to consider another model in the network: for the most reliable agentic coding on complex repositories, Kimi K2.6 remains strong (detailed analysis in our article on the best coding models), while MiniMax M2.7 remains a proven daily workhorse. Thanks to the unified pricing, experimentation is free—switching the model is just one line in your request.