Cline raises free DeepSeek Flash quota 3x for coding agents

• Cline made DeepSeek V4-Flash-0731 free in the extension in its rollout post, then tripled the free quota after saying the economics were sustainable in its quota note.
• The rollout spread across agent surfaces fast: Nous put the model 90% off for seven days in its Portal promo, OpenHands offered it free to cloud users in its cloud note, and OpenCode reported 8T Flash tokens on Aug. 1 in its usage post.
• The release kept the same small active footprint while jumping on agent tasks: Artificial Analysis described V4-Flash-0731 as 284B total, 13B active, 1M context, text-only, and $0.14/$0.28 per 1M input/output tokens in its benchmark note.
• The benchmark move was agent-heavy: DeepSeek’s model card screenshot in kimmonismus's release post shows DeepSWE rising from 7.3 to 54.4 and Terminal Bench 2.1 from 61.8 to 82.7 against Flash Preview.
• The cheap-token story has a harness caveat: Ramp found DeepSeek V4 Flash ran at 3x GPT-5.6 Luna’s cost per task on Ramp SWE-Bench in its cost breakdown, while other agent tools subsidized or gave away usage.

The Hugging Face model card says the 0731 checkpoint keeps the DSpark structure and uses an encoding folder instead of a Jinja chat template. DeepSeek’s Codex integration doc has developers write ~/.codex/models.json because only deepseek-v4-flash supports Codex today. Unsloth’s local guide puts lossless Q8 at 162 GB and says a 3-bit quant can run on a 110 GB RAM device. OpenCode’s 8T-token day explains why coding-agent vendors moved first.

Agent tools treated Flash 0731 like a quota reset, not a slow model-catalog update.

• Cline made the updated model free, with setup as npm i -g cline, Settings, Cline provider, then deepseek-v4-flash, in its launch post.
• Cline later said it had “3x’d the free quota” because it could afford the usage sustainably in its follow-up.
• Nous Research put DeepSeek V4 Flash 0731 90% off for seven days with Novita Labs, claiming it was over 1000x cheaper than Fable 5 on comparable tasks while still beating it on Terminal-Bench 2.1 in the Portal announcement.
• OpenHands made openhands/deepseek-v4-flash free for a limited time in its cloud rollout.
• OpenCode reported 8T Flash tokens on Aug. 1, split as 5T free usage and 3T on OpenCode Go, in its usage tally.
• Ollama shipped the model in cloud as ollama run deepseek-v4-flash:0731-cloud, then said it was over 2x faster than the previous day in its speed update.
• Baseten advertised the model on its Model APIs as 80 to 98% cheaper than other frontier models with 1M context in its API post.
• Venice added DeepSeek V4 Flash 0731 as a fully private option in its availability post.

DeepSeek’s public release centered on post-training, DSpark, and API compatibility.

The official model card calls V4-Flash-0731 the official release replacing the preview, with the same structure as DeepSeek-V4-Flash-DSpark and a speculative decoding module attached.

Key release details:

• API model ID: deepseek-v4-flash, according to DeepSeek's Codex integration docs.
• Scale: Artificial Analysis described it as 284B total parameters and 13B active at inference time in its release analysis.
• Context: 1M tokens, with text input and output only, per Artificial Analysis.
• License: the weights were published under MIT, which kimmonismus's post highlighted alongside the technical report.
• Speculative decoding: vLLM said the release carries the DSpark draft module inside the weights, so speculative decoding is one flag rather than a second model path in its serving note.
• Codex support: DeepSeek’s Codex doc says only deepseek-v4-flash currently supports Codex, with deepseek-v4-pro support expected in early August 2026.

The official table’s biggest deltas are on agentic coding, terminal use, and repo tasks.

Against Flash Preview, the benchmark table shown in kimmonismus's model-card screenshot implies these point moves:

• Terminal Bench 2.1: 61.8 → 82.7, +20.9 points.
• NL2Repo: 39.4 → 54.2, +14.8 points.
• Cybergym: 38.7 → 76.7, +38.0 points.
• DeepSWE: 7.3 → 54.4, +47.1 points.
• Toolathlon-Verified: 49.7 → 70.3, +20.6 points.
• Agents’ Last Exam: 15.8 → 25.2, +9.4 points.
• AutomationBench Public: 10.8 → 25.1, +14.3 points.
• DSBench-FullStack: 37.0 → 68.7, +31.7 points.
• DSBench-Hard: 25.8 → 59.6, +33.8 points.

Artificial Analysis put the broader jump at 40 → 50 on its Intelligence Index, +10 points, and GDPval-AA v2 at 1189 → 1559 Elo, +370 Elo, in its model note. It also said output token usage fell from roughly 234M to 206M on the Intelligence Index run, a 12% drop.

The hallucination improvement was narrower. Artificial Analysis's Omniscience note says the AA-Omniscience Index improved from -23 to -16 because hallucination rate fell to 84%, while accuracy stayed unchanged at 37%.

The release turned DeepSeek’s cache discount into the main economic fact.

Artificial Analysis priced first-party Flash 0731 at $0.14 per 1M input tokens, $0.28 per 1M output tokens, and $0.0028 per 1M cached input tokens, a 98% cache-hit discount, in its release note. It also placed Flash 0731 at roughly 60% lower cost per task than GPT-5.6 Luna max after OpenAI’s price cut.

Cline framed the same point as “cost per task, not cost per token” in its comparison, citing DeepSeek completing the same Artificial Analysis benchmark tasks as Fable at 105x lower cost. Nous went further for its promotion: its Portal post claimed a 90% discounted Flash 0731 was over 1000x cheaper than Fable 5 on comparable tasks.

ValsAI found the open-weight angle similar but slower. ValsAI's thread ranked Flash 0731 as the #3 open-weight model on the Vals Index, with 63.95% accuracy, $0.06 per test, and latency around 859.57 seconds.

First-run reports converged on long-horizon persistence, frontend strength, and rough polish.

Garrett Neubig said Flash 0731 was “really good” at long-horizon tasks and usually did not lose the plot, after using GPT Sol, Terra, and GLM-5.2 as daily drivers in his hands-on post. Omar Sar said Flash High was “insanely good at front end” and that HTML artifacts looked tuned for the model in his frontend test.

Simon Willison got a weak pelican at default reasoning, then a much better one after setting OpenRouter reasoning effort to high in his comparison. Rish said a long-running data extraction job improved recall from 71% to 79%, with Luna-medium at 67% at the same cost, in his production note.

The polish reports were split. H.C. Solakoglu said GPT-5.6 Luna was more polished and consistent, with less buggy code and fewer hallucinations, while V4 Flash was “exceptionally capable for its price” in his comparison. Rish also reported “503 service too busy” errors during the launch load in his capacity complaint.

DeepSeek’s own benchmark note quietly makes the harness part of the story.

The Hugging Face model card says public Code Agent tasks were evaluated with the unreleased DeepSeek Harness in minimal mode, max reasoning effort, temperature 1.0, and top_p 0.95. That benchmark harness is not what most users touched on day one.

Toyama nao’s Zhihu review, translated in Zhihu Frontier's post, said Claude Code can constrain Flash’s long planning when the model spends 50K thinking tokens before writing code, while Codex can require 30 to 40% more steps for the same task. The same review said DeepSeek’s upcoming native harness should remove many of those bottlenecks.

Omar Sar said DeepSeek V4 Flash worked well in Pi until DeepSeek’s own harness arrives in his Pi post. Teortaxes reported the opposite failure mode, saying Flash was burning 1M tokens regularly in Pi and looked “very harness-sensitive” in his complaint.

Ramp measured that cost directly. Compared with GPT-5.6 Luna on Ramp SWE-Bench, Ramp's thread said DeepSeek V4 Flash ran at 3x the cost per task, spent nearly 3x longer in the harness, and took more turns on every task. Ramp's follow-up said the model churned through many turns before its first code change, then showed persistence through repeated tests and revisions.

The open-weight release immediately turned into a memory-fit contest.

Unsloth said Flash 0731 can run lossless 4-bit on 168 GB RAM and 3-bit on 110 GB RAM in its local-running post. A later Unsloth update said smaller quants reached 1-bit to 4-bit, with 3-bit on 128 GB RAM and 1-bit on 96 GB RAM, in its quant update.

Daniel Hanchen explained the format gotcha in his quant note: DS4 uses MXFP4 for MoE layers and FP8 for most others, Q8_K_XL = MXFP4+BF16 is lossless, and Q4_K_XL = MXFP4+Q8_0 keeps 96% same top-1%. Teknium sized the raw load around 160 GB in FP4, plus more for context, in his DGX Spark reply.

vLLM described the model as a sparse MoE with 256 routed experts, six active per token, 1M context, and three reasoning-effort levels in its support note. Its DSpark vLLM flag was:

The small-box anecdotes followed. Garrett Neubig said the model could apparently be hosted on a single DGX Spark and was free on OpenHands Cloud in his follow-up. Cline said r/LocalLLaMA users were already running V4-Flash on a Mac M2 Ultra in its local-models post.

The rollout also exposed a provider-policy wrinkle around paid APIs that train on user data.

Teknium first said the model was available in Hermes Agent through Nous Portal and OpenRouter in his catalog post. Minutes later, his correction said Nous Portal would not allow models that train on user data through until more providers were available.

OpenRouter surfaced the issue as a routing block: the screenshot in Teknium's correction shows “Paid model training violation” and marks DeepSeek “Not routable.” Teknium later said the issue was resolved once more providers came online in his provider update.

Teknium also told one user that DeepSeek “explicitly does train on your data” in his privacy reply. In a separate reply, his OpenRouter clarification said the flag applied to “Paid APIs that train on your data,” not to zero-data-retention as a generic phrase.