Fetching from the wire…
Top 5 · 2026-06-15 · source-backed
DeepSeek dropped V4 in mid-June as an open-weight model with a 1-million-token context window, priced at $1.74 per million input tokens, posting near-parity with GPT-5.4 on math and Q&A benchmarks (MindStudio). That's the headline number. The architecture underneath is more interesting. It actually shipped as two MIT-licensed Mixture-of-Experts models, DeepSeek-V4-Pro (~1.6T total / 49B active) and DeepSeek-V4-Flash (~284B / 13B active), both with 1M context and a hybrid attention mechanism built to cut inference cost on long-running agentic tasks (LLM-Stats).
Here's the part that should make you stop scrolling: V4 posts the highest reported open-weights SWE-bench Verified score at roughly 80.6%, and Flash output runs around $0.28 per million tokens. Open weights, a real coding score, and pricing that's an order of magnitude under the closed frontier coding models.
I've run the self-host-vs-API math maybe four times in the last year and it always came out the same way. Renting from a frontier lab was cheaper than running my own GPUs once you factored in ops, idle time, and the fact that the open model was a tier behind on the work I cared about. That gap just closed. Not on every axis. GPT-5.4 and Fable 5 still win on the hardest reasoning. But for agentic coding loops where you're burning tokens by the millions on a long context, an 80.6% SWE-bench model at $0.28/M output changes the calculation in a way it hasn't before.
What I'd actually do: don't rip out your stack. Run a bake-off. Take your most token-heavy agent loop, the one whose monthly bill makes you wince, and route it to V4-Flash for a week behind a flag. Measure the quality delta on your real tasks, not the leaderboard. If Flash holds 90% of the quality at 10% of the cost on your workload, that's your new default for the bulk-token path, and you reserve the expensive model for the verification step. The economics finally support a tiered routing strategy where you weren't forced into it before. That theme of routing cheap-where-you-can shows up again two stories down.
Each link below shares sources, entities, or timing with this story.
DeepSeek released V4 on April 24 and the numbers demand attention. V4-Pro is 1.6 trillion parameters total with 49 billion active, MIT-licensed, native 1M-token context. It scores 80.6% on SWE-bench Verified, putting it within 0.2 points of Claude Opus 4.6. On Terminal-Bench 2...
DeepSeek-V4-Flash-0731 landed July 31 under MIT with a DSpark speculative-decoding module attached. Terminal Bench 2.1: 82.7. Toolathlon-Verified: 70.3. DSBench-FullStack: 68.7. DeepSWE: 54.4. NL2Repo: 54.2. The model card claims it beats DeepSeek-V4-Pro (Preview) "despite its...
GLM-5.1 from Zhipu AI scored 58.4% on SWE-bench Pro. GPT-5.4 scored 57.7%. Claude Opus 4.6 scored 57.3%. That's the first time an open-weight model has ever topped a major coding benchmark against the best proprietary models. The specs matter. GLM-5.1 is a 754B-parameter mixtu...
The Segment co-founder published "Small models have arrived" on August 26, and it took 703 points on Hacker News (calv.info). His measurement: a personalized-news task that cost about a dollar on Sonnet-class models now runs at about a dime. Ten times cheaper, doing the job we...
The leaderboard says first place. The methodology says you should check your own bill. Qwen3.8 Max now ranks first on Artificial Analysis' agentic index, scoring 86.1 on OSWorld-Verified ahead of GPT-5.6 Sol Max at 83.2 and Fable 5 at 85.0, priced at $2.00/M input and $6.00/M...
Simon Willison doesn't hand out superlatives. So when he writes that Z.ai's GLM-5.2 is "probably the most powerful text-only open weights LLM," that's worth stopping for. His June 17 evaluation walks through a 753B-parameter Mixture-of-Experts model with 40B active params, a 1...
MindPattern daily
One email a day at 7 AM. Sources and a take on every story. Unsubscribe anytime.