On 2026-10-29, the same day it open-weighted Large 4, Mistral set what had been a 50%-off, no-end-date launch sale as the official list price: $0.70 input / $0.07 cached input / $2.10 output per 1M tokens, down 48.5%/50%/49.8% from the pre-sale list price of $1.36/$0.14/$4.18 — but only 2.9%/0%/0.5% above the original sale rate. This site flagged the undated sale on 10/10 as a risk the vendor was quietly shifting onto developers. Three weeks later, Mistral kept the sale price instead of letting it vanish.
Microsoft Foundry's model retirement schedule doesn't track the vendor's own clock. Claude Haiku 4.5 is scheduled to retire on Foundry on 2026-11-15 (two weeks out, with no replacement listed), even though Anthropic's own Claude API, AWS, and Google Cloud still mark it Active. The reverse happened with Mistral Medium 3 (retired on Mistral's own platform 2026-08-31) and Ministral 3B (retired 2025-12-31): Foundry kept both at GA until 2026-12-09, 100 and 343 days after the vendor pulled the plug. Anthropic's own docs say Foundry "follows the Claude API lifecycle schedule," but the actual dates don't match.
Mistral put Large 4 (Le Chonk) into public preview on 2026-10-06. The official announcement and model card both list $1.36 input / $4.18 output per 1M tokens (cached input $0.14), but the actual billing page runs a straight 50% launch sale: $0.68 input / $2.09 output (cached $0.07). No end date is published anywhere. This is the second time this series has caught the same move — Gemini 4 Argon did it on 9/30. Weights and a named license are still due by end of month.
Mistral Large 4 (ML4, nicknamed Le Chonk): public preview announced 2026-10-06, a 1.05T-total-parameter granular MoE multimodal model; the official active-parameter count is inconsistent (the launch post says 49B, the docs model card changed to 52B a few hours later with no explanation); list pricing is $1.36 input / $4.18 output per 1M tokens (cached $0.14), though the docs card currently shows exactly half that with no explanation; the docs card claims a 1M-token context window, but third-party trackers Artificial Analysis and Vals.ai both list 512K (524,288 tokens) with a 256K output cap; scores 93% on Cybench and 82% on Artificial Analysis Cyber Index's vulnerability-reproduction task (self-reported, the highest of any model — Claude Opus 5.5 and GPT-6 Astra score near zero by refusing); but on the independent Artificial Analysis Intelligence Index v4.3.2, it scores only 38, trailing MiMo-V2.6-Pro (46), GLM-5.3 (45), and Kimi K3 (44) by 6-8 points; Vals.ai ranks it 6th of 75 models (1st among open models) on Harvey's Legal Agent Benchmark, one of its few independently-verified strengths; open weights and a named license are still pending, expected by end of month (reporters were told October 27)
Mistral closed a €3B Series D led by Samsung Electronics, with valuation jumping from €11.7B a year ago to over €21B. This round signals that Mistral isn't betting on model benchmarks — it's selling 'sovereignty' itself as a product to European governments and enterprises unwilling to hand AI workloads to US clouds.
Mistral is Europe's most successful AI startup, cutting through the market with a 'smaller, faster, cheaper' strategy and European data-sovereignty positioning. Mistral Large 3 is Europe's strongest commercial LLM, Small 4 is the 24B efficiency king, and Medium 3.5 is the open Modified-MIT model optimized for agentic coding. Its moat is not technical scale but the 'European compliance' card.
For side projects, toy demos, and RAG prototypes, nobody wants to swipe a credit card on day one. This is a verified roundup of 40+ LLM inference providers still operating as of 2026/05, tiered by whether free resources auto-replenish or are one-time grants. Each entry notes credit-card requirements, supported models, paid starting prices, and catches. Chinese-origin providers including Zhipu GLM (permanently free), Doubao (2M tokens/day), Kimi, DashScope, and the Ollama local option are all included.
The main on-device LLMs in 2026 are Gemma 3n, Qwen 3.5 Small, Llama 3.2, Phi-4-mini, Ministral 3, and SmolLM3. Sub-3B quantized models can hit 30-50 tokens/sec on phones with 8GB RAM, but RAM, thermal throttling, and context window remain hard constraints.
2026 Q1 saw a full-blown open-source model explosion: on the LLM front, GLM-5, Kimi K2.5, and Qwen3.5 caught up with closed-source models; Embedding and Reranker are dominated by Qwen3 and BGE; speech has Voxtral TTS and Whisper V3; image has FLUX.2; and video has Wan 2.2 rivaling Sora. This is the complete navigation map.
The official provider directory now lists 60 entries. The most common failure when attaching a local model is writing Ollama's base URL with /v1 — that breaks tool calling, and the model starts emitting raw tool-call JSON as plain text.