Model releases

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New models, open releases and updates: flagship launches, open weights, and price and performance changes as they happen.

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81–88 of 88

Jul 11, 2025Friday

Mistral AIJul 11, 00:00

Mistral releases Devstral Medium and upgrades Devstral Small 1.1

Mistral AI worked with All Hands AI to launch Devstral Medium and upgrade Devstral Small 1.1.

Why it matters: Mistral and All Hands AI jointly released two coding agent models with SWE-Bench Verified scores and API pricing, making comparison with existing options easier.

Jun 10, 2025Tuesday

Mistral AIJun 10, 20:00

Mistral AI releases its first reasoning model, Magistral, in open and enterprise versions

Mistral AI released Magistral, its first reasoning model, in two versions: the 24B open-source Magistral Small and the enterprise Magistral Medium.

Why it matters: Mistral's first reasoning model comes in two versions with parameter counts and AIME2024 results, so you can judge its open-source and commercial positioning.

May 21, 2025Wednesday

Mistral AIMay 21, 20:00

Mistral AI releases agentic coding model Devstral under Apache 2.0

Mistral AI and All Hands AI released Devstral, an agentic LLM for software engineering tasks, under the Apache 2.0 license. It scores 46.8% on SWE-Bench Verified, more than 6 points above the previous open-source state of the art.

Why it matters: A joint Mistral and All Hands AI agentic coding model, with its SWE-Bench Verified score and the bar for local deployment.

May 7, 2025Wednesday

Mistral AIMay 7, 20:00

Mistral AI releases Mistral Medium 3, targeting low cost and enterprise deployment

Mistral AI released Mistral Medium 3, which it says reaches or exceeds 90% of Claude Sonnet 3.7 across benchmarks. Pricing is $0.4 per million input tokens and $2 per million output tokens.

Why it matters: Mistral Medium 3 benchmarks against Claude Sonnet 3.7 at lower cost, and the post gives a path to private enterprise deployment and customization.

Mar 17, 2025Monday

Mistral AIMar 17, 20:00

Mistral AI releases Mistral Small 3.1 with multimodal support and 128k context

Mistral AI released Mistral Small 3.1, which improves text performance and multimodal understanding over Mistral Small 3 and extends the context window to 128k tokens. Inference runs at 150 tokens per second, and the model is open-sourced under Apache 2.0.

Why it matters: Mistral gives the multimodal, 128k-context and 150 tokens/s figures for Mistral Small 3.1, letting readers compare it with small models of the same class.

Mar 12, 2025Wednesday

Hugging Face BlogMar 12, 08:00

Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM

Google announced an open LLM called Gemma 3 and named three traits in the title: multimodal, multilingual, and long context. The RSS snippet has no body, so parameter size, context length, license, and benchmark results are not disclosed. Watch the full post or model card; “open” does not equal open source from the title alone.

Why it matters: A new Gemma release from Google is inherently newsy, and the multimodal/long-context/open framing hits HKR-H and HKR-R. HKR-K misses because the feed gives no specs, context window, license, or benchmark data, so this lands at the low end of featured.

Oct 1, 2024Tuesday

OpenAI NewsOct 1, 18:02

Model Distillation in the API

OpenAI launched an API distillation workflow on October 1, 2024, letting developers use outputs from GPT-4o and o1-preview to fine-tune cheaper models such as GPT-4o mini. The suite includes Stored Completions, Evals in beta, and fine-tuning; setting store:true auto-saves input-output pairs with no added latency, per the post. Pricing includes 2M free GPT-4o mini training tokens per day and 1M for GPT-4o through October 31; Evals are free up to 7 runs per week through year-end if shared with OpenAI.

Sep 12, 2024Thursday

OpenAI NewsSep 12, 18:03

Introducing OpenAI o1

OpenAI released o1-preview and o1-mini on Sept. 12, 2024, with access for ChatGPT Plus, Team, and tier-5 API developers. The post cites 83% vs 13% on an IMO qualifier, 84 vs 22 on a jailbreak test, and says o1-mini is 80% cheaper than o1-preview. The tradeoff is clear: the API lacks function calling, streaming, and system messages, and the models do not yet support browsing or file and image uploads.

Why it matters: A major OpenAI reasoning-model launch with all three HKR signals: HKR-H from the new “think before answering” hook, HKR-K from concrete benchmark, safety, and pricing numbers, and HKR-R from the tradeoff practitioners must manage between stronger reasoning and missing API basics.

Take

OpenAI released o1-preview and o1-mini on September 12, 2024, and my read is blunt: this was not a normal model refresh. It was OpenAI admitting that bigger pretraining plus smoother UX was no longer enough. The headline numbers are strong: 83% versus 13% on an IMO qualifier, and 84 versus 22 on a jailbreak test. The product tradeoff is just as revealing: the API shipped without function calling, streaming, or system messages. That tells you OpenAI was willing to ship an awkward preview if it could first establish a new axis of progress: spend more inference-time compute, get better reasoning.

I think this mattered more than many people realized at the time. Through most of 2024, the field was fixated on multimodality, lower latency, longer context, and cheaper inference. GPT-4o was the clean expression of that strategy: one model for most interactions. Anthropic was winning mindshare with Claude 3.5 Sonnet as a practical coding model. Google kept pushing the long-context story around Gemini. o1 changed the competitive question. Instead of asking who had the most general assistant, OpenAI asked who could think through the hardest tasks with extra deliberation. A lot of what the market did in the following year was basically a response to that move.

What I buy here is not the anthropomorphic line about “thinking like a person.” It is the willingness to separate capability from usability. Most vendors hide product gaps when they launch a new model. OpenAI did the opposite. It stated plainly that o1 lacked browsing, file and image uploads, and several core API features. That makes this feel closer to a research preview than a finished platform primitive. Honestly, that increased its credibility for me. If this had been a simple rebrand of GPT-4o with better marketing, there would have been no reason to expose the rough edges so openly.

I still have real reservations. First, the 83% versus 13% IMO result is eye-catching, but this post does not disclose the test protocol in enough detail. How many problems? Single attempt or repeated sampling? Best-of-N or fixed budget? Math benchmarks are especially sensitive to search and compute allocation. We have seen this before. DeepMind’s earlier work in formal math and geometry showed that impressive scores can reflect genuine method improvements, but also that evaluation setup changes the story a lot. OpenAI links out to a technical post, which is fine, but if I judge only this launch article, I would not treat 83% as a direct proxy for stable real-world productivity.

Second, I am cautious about the jailbreak score jump from 22 to 84. Higher is better, obviously. But safety numbers are the easiest place for vendor narratives to outrun what practitioners can verify. The article says this was “one of our hardest jailbreaking tests,” but it does not say how broad the test set was, what failure categories dominated, or whether the gain came from stronger refusal behavior versus better contextual policy reasoning. Reasoning models do create a new safety opportunity: if the model can reason about policy before answering, it has another chance to avoid harmful output. They also create a new attack surface. Later debates around reasoning traces, hidden chain-of-thought, and policy leakage all grew out of this tension.

There is another signal here that I think was underread: OpenAI “reset the counter back to 1” and named the series o1. That is more than branding. It tells developers that capability scaling is no longer one linear sequence of GPT-4, 4.5, 5, where each step is just broader and smoother. OpenAI was creating a second branch: slower, more expensive per task, but much better at difficult reasoning. That branch became strategically important because enterprise buyers will pay for higher-confidence performance on complex tasks, as long as the latency and integration pain stay manageable. At launch, o1 had not solved that second condition. So this looked more like a directional declaration than an end-state product.

The 80% cheaper pricing for o1-mini also deserves more attention than it got. Cheap small models are common. Cheap reasoning models are less common, because per-task cost depends not only on model size but also on how long the model “thinks.” OpenAI pushing a mini variant this early suggests a two-tier market strategy. One tier sells the ceiling on hard reasoning. The other sells an acceptable cost structure for coding and structured workflows. Later vendors copied the same pattern with flagship reasoning models and lighter versions. This article does not disclose the actual price points, so I am not going to invent unit economics, but the 80% delta already tells you OpenAI was trying to lower the experimentation threshold for developers.

My broader view is that o1 did not matter because it was the first model to “reason.” The field already had chain-of-thought prompting, self-consistency, tree search variants, and a long academic history of trying to buy better answers with more test-time computation. o1 mattered because OpenAI turned that idea into a front-line product strategy, and accepted a meaningful product regression to do it. That is rare. It also hints at internal pressure. If OpenAI had stayed on the pure chat-assistant track, it may not have created enough separation on hard tasks.

My pushback is simple: stronger reasoning does not automatically mean stronger workflows. If the API lacks function calling and system messages, developers cannot cleanly wire the model into production agents. Solving more benchmark problems and failing less often inside a real tool-using pipeline are different achievements. OpenAI clearly demonstrated the first one here. It had not yet delivered the second.

So my verdict on this launch is: high research significance, limited platform readiness. The capability curve moved. The product surface was still incomplete. With hindsight, the direction was right. On launch day, judged from this article alone, o1 looked less like a mature deployment and more like OpenAI planting a flag on where the next capability race would be fought.