DeepSeek V4.1 Flash Astra

DeepSeek-V4.1-Flash on Fireworks: Astra-level DeepSWE at 1/15th the cost

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Deepseek V4.1 Flash Astra DeepSeek-V4.1-Flash on Fireworks: Astra-level DeepSWE at 1/15th the cost

PUBLISHED 9/14/2026

Table of Contents DeepSeek on the DeepSWE Pareto Frontier Under the hood: the new split activation architecture The KV cache optimizations Terminal-Bench 2.1: One point behind Astra at 12x cheaper HLE and an oracle router eval Get started with DeepSeek-V4.1-Flash

Table of Contents

Last week, Fireworks released the latest DeepSeek-V4.1-Flash . After putting it through a full suite of benchmarks, the results show it sets a new pareto frontier across key tradeoffs like quality, speed, and cost. On DeepSWE, it hits GPT-6 Astra-level coding accuracy at 1/15th the price per task. Developers often ask us how to pick the right model. The reality is that a model is just a single point on a map, but your application is a full system. AI capabilities are jagged, meaning a model that handles one task brilliantly might struggle with another. Because no single model wins across every scenario, workload design comes down to matching the right model to the right task, whether you need low-latency processing or deep computation for complex reasoning. Figure 1: Your Workload is a System of Tasks Here is how DeepSeek-V4.1-Flash actually performs across different workloads and where it could fit into your stack. DeepSeek on the DeepSWE Pareto Frontier

We evaluated the DeepSWE benchmark to see how models balance accuracy against cost per task. Model Effort Pass@1 Cost/Task DeepSeek V4.1-Flash max 74.34% $0.430 GPT-6-Astra xhigh 74.12% $6.524 Gemini 3.8 Flash high 73.83% $2.362 Claude Opus 5 max 73.65% $11.838

On quality, these four models are the same class. On DeepSWE, all four land within 0.7 points on pass@1, and our run-to-run variation is 1.4 to 3.2 points, so no model in this group is far ahead of the others on quality. The differentiation comes entirely on cost: DeepSeek-V4.1-Flash on Fireworks delivers the same accuracy band at $0.43 per task, 5.5x below Gemini 3.8 Flash, 15x below GPT-6 Astra, and 28x below Claude Opus 5. The new DeepSeek-V4.1-Flash model fundamentally changes the unit economics of autonomous software engineering tasks. Running high-iteration agent workflows, multi-turn bug sweeps, or large-scale test generation on expensive closed models like GPT-6 Astra and Opus 5 rapidly drains engineering budgets. At $0.43 per task, frontier-grade SWE agents become viable as continuous, background infrastructure. With DeepSeek V4.1 Flash, you can now run 15 autonomous coding attempts for the price of a single Astra call. Under the hood: the new split activation architecture

DeepSeek-V4.1-Flash is the smallest model in DeepSeek's new architecture family, and comes with some new architectural enhancements. It is composed of 552B-parameter MoE with a new encoder–decoder split architecture. In this architecture, instead of one activation budget for an entire forward pass, the model splits it into 8B active parameters for input, and 16B for output . This results in a more efficient design, resulting in more economical token spend. That asymmetry aligns with how coding agents operate. A typical agent trajectory is very input-heavy. A coding agent often re-reads files, re-reads its own scratchpad, re-reads tool output, and emits a comparatively small patch. In our DeepSWE run, the model consumed 36.9M input tokens per task against 211K output tokens, a 174:1 ratio. This architecture enables developers to spend half as much compute on the input side, which is typically where a larger percentage of tokens exist.

The KV cache optimizations

DeepSeek improved the KV Cache utilization on DeepSeek-V4.1-Flash compared to previous generation DeepSeek V4, only consuming ¼ the HBM, and ⅛ the SSD Storage. Figure 2: KV Cache on DeepSeek-V4.1-Flash This new KV Cache optimization unlocks better economics for your coding agents. From the DeepSWE run, we had the following economics: Cost Component Tokens/Task Rate Cost/task Share Uncached input 148,741 $0.22/M $0.0327 7.6% Cached input 36,744,586 $0.007/M $0.2572 59.9% Output 211,513 $0.66/M $0.1396 32.5% Total $0.4295

99.6% of input tokens on this run were cache hits, and cache-hit charges are 60% of the bill. Most people assume your agent economics results in you mostly paying for what the model generates. However in a long-running agent loop, the majority of the spend comes from re-reading the same context repeatedly. This spending loop from repeat tasks, makes a 4x reduction in KV cache memory have a greater impact on your token spend. It directly targets the biggest cost driver in your agent design, bringing a 74%-class DeepSWE score down by 15X. The 8x reduction in SSD overhead plays a complementary role to the HBM optimization, by keeping cache hit rates near 99% across long trajectories. Terminal-Bench 2.1: One point behind Astra at 12x cheaper

On a different task, we see something similar DeepSeek-V4.1-Flash is approaching the frontier at a fraction of the cost. Model Accuracy GPT-6-Astra 87.5% DeepSeek-V4.1-Flash 86.5%

The results on Terminal-Bench 2.1 are one point a part on quality, but the economics is significantly different. DeepSeek-V4.1-Flash uses roughly 4x more output tokens on average than Astra, meaning it thinks longer and writes more.The results is 20x most cost optimized on output pricing alone. When you combine everything, including uncached input, cache hits, and output, DeepSeek-v4.1-Flash results in 12x lower cost per task at almost the same quality. HLE and an oracle router eval

We ran a third benchmark task, Humanity’s Last Exam, also known as HLE. This is a multi-modal benchmark to measure the limits of the LLMs across graduate-level academic subjects. In this testing, we also performed some benchmarking with oracle routers, similar to what we have done with Kimi K3 and DeepSeek V4 Pro . As a quick refresher: oracle routing is a method for measuring the best theoretical performance by running the task through each model and then picking the cost-optimized correct option to establish the cost and performance ceiling. Configuration HLE Score DeepSeek-V4.1-Flash 34.52% GPT-6-Astra 50.40% Oracle Router (Best-Of GPT-6-Astra + DeepSeek-V4.1-Flash) 54.80%

On HLE, DeepSeek-V4.1-Flash trails Astra when run alone. This shows that DeepSeek alone is better suited for agentic...