August 4, 05:26

DeepSeek V4 Flash Analysis: 8 Trillion Tokens Per Day, 85x Price Gap, and Impact on Mid-Tier Models in Agent Economics

The DeepSeek V4 Flash model demonstrated massive demand, consuming 8 trillion tokens in a single day on the OpenCode tool. This volume exceeded the average daily usage of the OpenRouter platform, which is around 6.6 trillion tokens. This surge in usage reflects a fundamental shift in AI interaction methods: where tasks used to be simple question-and-answer exchanges, agents now perform more complex operations such as reading files, modifying code, running programs, and retrying after errors, which significantly increases token consumption.

With the advent of agent-driven tasks, the evaluation criterion for models has shifted from the quality of a single response to the cost of completing a long-running task. For instance, one million output tokens cost 2 yuan with DeepSeek, whereas the same operation with Claude Opus 4.8 cost $25 USD, equivalent to approximately 170 yuan, demonstrating an 85-fold price difference. Although the V4 Flash Max model scored 50 points compared to 56 for Opus Max in the Artificial Analysis Intelligence Index v4.1, the cost to pass this test was $72.02 for V4 Flash versus $3752.55 for Opus, with Opus gaining 6 extra points at roughly a 52-fold increase in call costs.

The first to feel the impact of this trend are not the weakest models, as smaller models remain suitable for routing, and flagship models continue to handle the most complex tasks. Mid-tier models are the most vulnerable—they are stronger than V4 Flash but cost dozens of times more and are not powerful enough to solve the tasks that V4 Flash handles. This is DeepSeek's 'killer line': models do not necessarily need to be the strongest; they just need to be good enough to perform most tasks, leveraging a price gap of almost two orders of magnitude to displace more expensive options from the default list.

The ability to sell Mutai at mineral water prices is due to the direct inference cost per token, which depends on activated parameters, accuracy, attention, KV cache, output length, hardware utilization, and parallelism. V4 Flash has 284 billion total parameters but activates about 13 billion per token by using hybrid attention, low-precision weights, and cache optimization. With a context of one million tokens, the inference computational load is about 10% of V3.2, and the KV cache size is about 7%. The V4 series overhauled the attention mechanism, implementing CSA compress-then-focus and HCA full-browse mechanisms, and utilizes DeepSeekMoE routing, directing each token to a small subgroup of experts. Commercial pricing and service costs follow these technical features. Thus, the 'killer line' redefines the boundary of development: the combination of performance and price becomes the decisive factor, where the cost of task execution is the determining metric.

pandaily.comAugust 3, 10:53

MiniMax releases open-source model H3, balancing Kimi K3's performance with DeepSeek's pricing

MiniMax released its first open-source multimodal generative model, H3, on July 31. This model supports processing text, images, audio, and video, offering direct output in 2K resolution and generating audiovisual content up to 15 seconds long. The release of H3 followed the M3 model, which was presented five months earlier, but its unsatisfactory performance caused a 15% drop in stock value in a single day.

In July, the industry faced a certain 'line of survival': Moonshot AI unveiled the weights of the Kimi K3 model, while DeepSeek lowered the cost of the V4-Flash cache to 0.02 yuan per million tokens. The Kimi K3 model, boasting 2.8 trillion parameters, became the world's first open model at the trillion-parameter scale, ranking fourth globally and first among open-source models. The output cost of the DeepSeek V4-Flash model is about $0.28 per million tokens, which is roughly 90 times cheaper than Claude Opus 4.8. A model that cannot surpass Kimi K3 in performance and lacks DeepSeek's pricing strategy risks disappearing from the market.

Model H3 occupies an intermediate position: it ranks first globally in video editing according to the Artificial Analysis leaderboard, enabling 2K generation for 0.8 yuan per second, which is three times cheaper than comparable flagship solutions. Nevertheless, it is not a leader in either overall performance or price. The choice of open source is viewed as a more passive step aimed at lowering thresholds in the AI field, attracting chip manufacturers and developers for optimization compatible with domestic chips, and securing its place in the video segment dominated by Seedance through ecosystem and developer scale. The commercial value of open source will be tested by financial indicators.

Open source is a double-edged sword: in the long term, it creates ecosystem advantages deeper than the patents of closed systems, as demonstrated by Linux and Android. However, in the short term, it foregoes direct monetization, and open source itself does not generate revenue. Open-source companies typically have revenues an order of magnitude smaller than their closed competitors: OpenAI earns about $13 billion, and Anthropic about $45 billion annually, while Seedance 2.0's monthly revenue exceeds 100 million yuan. Open source can gain reputation, community, and developers, but so far has failed to ensure profitability.

MiniMax's 2025 financial reports show strain: revenue reached $79.04 million, a 158.9% increase, with gross profit at $20.08 million and a margin of 25.4%. However, Research and Development (R&D) expenses rose by 33.8% to $253 million, mainly due to cloud training costs and more than triple the volume of output. Adjusted net loss in 2025 reached $251 million compared to $240 million in 2024: revenue increased by one and a half times, while losses remained at the same level, driven by investments of similar scale rather than efficiency gains. Building an open-source platform requires developer tools, community operations, corporate support, and chip adaptation, and each aspect demands capital. The question arises as to how long shareholders will patiently wait for a company forced to continue burning cash in search of a more valuable direction. MiniMax has bet on overcoming the line of survival through ecosystem and extreme cost-performance ratio, rather than parameter scale.

DeepSeek V4 Flash Analysis: 8 Trillion Tokens Per Day, 85x Price Gap, and Impact on Mid-Tier Models in Agent Economics

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MiniMax releases open-source model H3, balancing Kimi K3's performance with DeepSeek's pricing