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AI Developer Digest

Thu, Jun 25, 2026

18 signals that cleared the gate24 min read
The Signal — start here
The defining story today is infrastructure: OpenAI unveiled Jalapeño, its first custom LLM inference ASIC co-designed with Broadcom — a reticle-sized chip built in nine months, targeting inference efficiency far beyond current GPUs. This is not a developer product today (no API, no pricing change announced), but it signals that the frontier labs are building their own silicon in earnest. Separately, the Gemini image preview models that were on a "Worth Watching" countdown in yesterday's digest are now gone — they shut down this morning (June 25), completing Google's most compressed deprecation cycle in the image generation space. The rest of today's digest is tooling: Mistral shipped meaningful enterprise access controls for connectors, and llama.cpp landed SYCL dual-GPU tensor parallelism with real throughput numbers on Intel Arc hardware.
Must-reads today
1
OpenAI + Broadcom Jalapeño chip — the first OpenAI custom inference ASIC, nine-month build cycle, not developer-facing today but worth watching for pricing and capacity signals starting late 2026
2
Gemini preview image model shutdown (TODAY) — gemini-3.1-flash-image-preview and gemini-3-pro-image-preview are down as of this morning; if you used them as an interim migration step from the June 24 Imagen shutdown, you are broken right now

Breaking Changes

1
●Breaking

Gemini Image Preview Models Shut Down — `gemini-3.1-flash-image-preview` and `gemini-3-pro-image-preview` Retired as of June 25

What changed
gemini-3.1-flash-image-preview and gemini-3-pro-image-preview were retired as of today, June 25, 2026. This completes the final step of Google's image generation stack deprecation cycle that began with the Imagen 3 model shutdown on June 24. Any call to either preview model ID now returns an error.
TL;DR
Both Gemini image preview model IDs are retired as of June 25; the General Availability replacements — gemini-3.1-flash-image and gemini-3-pro-image — have been available since May 28, 2026 and now include video-to-image generation support.
Developer signal
If you migrated to either preview model ID as an interim step following the Imagen 3 shutdown on June 24, your image generation calls are failing right now. Update the model string to gemini-3.1-flash-image (was: gemini-3.1-flash-image-preview) or gemini-3-pro-image (was: gemini-3-pro-image-preview). No code changes beyond the model string are required for existing generate_content() + GenerationConfig callers — the GA versions use the same API surface as the preview versions. The GA gemini-3.1-flash-image model adds video-to-image generation not available in the preview: pass video files via direct upload or public YouTube URLs alongside a text prompt. The gemini-3-pro-image GA version does not add video input. A note on what's still pending: imagen-3.0-capability-001 (Vertex AI mask-based editing) shuts down June 30 with no announced replacement — this is separate from today's shutdown and affects a different capability class. Video generation models also shut down June 30; migrate to Veo 3.1 preview model IDs.

Affects you ifYou call gemini-3.1-flash-image-preview or gemini-3-pro-image-preview in any production or development code; or you migrated from Imagen 3 models to preview model IDs as an interim measure on June 24.EffortQuick (model string swap only; API call pattern unchanged for existing generate_content() callers; test video-to-image on flash variant if you want the new capability).

Model Releases

No new model releases in the June 24–25 scan window.

  • LMArena top 5 unchanged as of June 21 (last confirmed update): Claude Opus 4.8, GPT-5.5 Pro, Gemini 3.1 Pro Preview, Claude Opus 4.7, GPT-5.5. No new model additions June 24–25 confirmed.
  • GLM-5.2 (June 13/17), Claude Fable 5 restoration (June 18), and other recent model events are outside the 24h window.

API & SDK Changes

1
Medium

Mistral Connector Controls: Enriched Admin Controls and Scoped API Keys Now GA

What changed
Mistral shipped four GA capabilities for connector access control: (1) Enriched Admin Controls — per-workspace connector access and per-tool on/off within a connector; (2) API Keys with Connector Scopes — create keys that access only workspace-shared connectors, or shared + private connectors; (3) Multi-account connectors — authenticate to a single connector with multiple accounts; (4) Connectors in Vibe Code — reuse configured connectors in the developer interface. A fifth item — Connectors Debugger — shipped in Public Preview for end-to-end root cause analysis on MCP connector failures.
TL;DR
Mistral connectors now have workspace-level access control, tool-level on/off per connector, and scoped API keys that prevent automated jobs from inheriting more access than they need — the four GA features are live on the Mistral API as of June 24.
Developer signal
If you run automated workloads (pipelines, agents, scheduled jobs) that access Mistral connectors, update your API key setup to use connector-scoped keys: create a key for the automated job and set its scope to only the connectors that job needs, rather than using a full-access key. This is a security hardening step that eliminates the common pattern where an automated process holds the same connector access as the human who wrote it. For enterprise teams: use the new workspace-level connector controls to partition connector access by team — you can now grant the engineering workspace access to GitHub connectors while blocking the same connector from the marketing workspace. The per-tool disable is particularly useful for sensitive connectors: if you want a knowledge-base connector to allow read but not delete_file, set that at the connector level without creating a separate connector or custom integration. The Connectors Debugger (preview) provides a trace view for MCP connector failures — useful if you've had unexplained tool call failures in Vibe or the API.

Affects you ifYou use Mistral connectors (built-in or MCP) in production workloads; you run automated pipelines that call connectors via API keys; you are an org admin managing connector access across multiple teams.EffortModerate (existing connector integrations continue to work without changes; adopting scoped keys and per-tool controls requires creating new key configurations and reviewing existing connector tool permissions; Vibe Code connector reuse requires no migration for existing users).

Research

Nothing cleared the quality bar this period. arXiv cs.AI and cs.CL access continues to return HTTP 403 on direct list fetch (consistent with prior digests). Search-based fallback found no papers from recognized labs (DeepMind, Meta FAIR, CMU, Stanford, MIT, AI2, Mila, ETH, Oxford, Google, Microsoft, Anthropic) with both a linked code repository and measurable benchmark numbers submitted June 24–25.

Tooling

2
High

OpenAI and Broadcom Unveil Jalapeño: First Custom LLM Inference ASIC

What changed
OpenAI unveiled Jalapeño, its first custom silicon, co-developed with Broadcom as a purpose-built LLM inference ASIC. This is the first chip in a stated multi-generation compute platform. Previously, OpenAI inference ran entirely on third-party hardware (primarily NVIDIA GPUs). Jalapeño went from initial design to manufacturing tape-out in nine months — claimed to be one of the fastest ASIC development cycles for high-performance advanced semiconductors. Early engineering samples are running GPT-5.3-Codex-Spark workloads in a test environment. Planned production deployment begins late 2026 across OpenAI data centers.
TL;DR
Jalapeño is a reticle-sized ASIC purpose-built for LLM inference, with significantly better performance-per-watt than current leading-edge alternatives by OpenAI's own benchmarks; no developer-facing API, pricing, or capacity changes are announced — developer impact will arrive via latency, cost, and throughput improvements when the chip enters production deployment in late 2026.
Developer signal
There is nothing to change in your code today. Jalapeño is an infrastructure announcement, not a product or API launch. The developer-facing effects will materialize as lower inference costs, higher throughput limits, or lower latency if OpenAI passes infrastructure savings through to API pricing — this is unconfirmed. What you can do now: note that OpenAI's inference stack will run on homogeneous, purpose-built silicon starting late 2026, which should reduce the variance in inference performance that comes from mixed GPU fleet deployments. For teams that compete with or benchmark against OpenAI's APIs: cost-per-token for OpenAI's internal inference is likely to improve meaningfully if the chip performs as described — model this in your build-vs-buy analysis. For teams building on third-party inference providers that use OpenAI models: provider pricing signals in Q4 2026 and Q1 2027 will reveal whether infrastructure savings flow through. The Jalapeño ASIC is not available for sale or rental — OpenAI and Broadcom have not announced licensing or cloud access.
Affects you ifYou are doing competitive infrastructure planning or build-vs-buy analysis on LLM inference costs; you are building products where OpenAI API pricing or capacity is a constraint and want to watch for changes in Q4 2026.EffortN/A — no code changes or migrations required at this time.
Notable

llama.cpp b9788: SYCL Tensor Parallelism for Dual Intel Arc GPUs — +78.6% Decode Throughput on Llama-3.3-70B

What changed
b9788 adds --split-mode tensor support for SYCL-based builds on dual-GPU configurations. The implementation uses a degenerate ring all-reduce pattern optimized for two GPUs: small tensors (< 32,768 elements) use FP32 direct memory copy with per-device addition kernels; large tensors (≥ 32,768 elements) use BF16 compression, halving PCIe bandwidth usage. Memory management uses one persistent uint8_t buffer per device (4 × nelem bytes) with a single allocation/deallocation lifecycle per device. Previously, SYCL builds only supported --split-mode layer (full model layers assigned per GPU). Tensor mode was already available for CUDA builds.
TL;DR
llama.cpp b9788 brings tensor-split multi-GPU to Intel Arc (SYCL): on dual Intel Arc Pro B70 GPUs, Llama-3.3-70B Q4_K_M hits 377.08 t/s prefill (+20.2% vs layer mode) and 17.40 t/s decode (+78.6% vs layer mode); Qwen3-Coder-Next-80B-A3B Q3_K_M achieves 216.56 t/s prefill (+38.3%) and 17.60 t/s decode (+23.0%).
Developer signal
Update to b9788 if you run llama.cpp on dual Intel Arc GPUs (Arc Pro B70 or similar) and are splitting large models across two devices. Change your launch flag from --split-mode layer to --split-mode tensor — this is the primary change; all other flags remain the same. The decode throughput improvement (+78.6% on Llama-3.3-70B) is the more practically significant number: layer mode is naturally better at prefill (each GPU runs different layers in parallel), while tensor mode excels at decode (both GPUs work on every layer simultaneously). Tensor mode's advantage over layer mode scales with model size relative to per-GPU VRAM — if you are loading a model that barely fits across two GPUs in layer mode, tensor mode will show the largest gains. The BF16 compression for large tensors means bandwidth between GPUs is not the bottleneck even on PCIe-connected discrete GPU pairs. The performance numbers above are from a dual Arc Pro B70 setup; results on other Arc hardware (Arc 770, Arc B580) will differ based on GPU-to-GPU bandwidth. No API, model, or quantization changes are required — drop-in binary update.

Affects you ifYou run llama.cpp inference on dual Intel Arc GPUs (any SYCL-capable Intel discrete GPU pair) and split large models (30B+) across both devices.EffortQuick (update binary, change --split-mode layer to --split-mode tensor in your launch command; no model or config changes).

Benchmarks & Leaderboards

No leaderboard movements or new SOTA entries in the June 24–25 scan window.

  • LMArena: No new model additions confirmed June 24–25. Last confirmed addition: seed-2.1-pro-preview to Code Arena on June 21. Top 5 unchanged (Claude Opus 4.8, GPT-5.5 Pro, Gemini 3.1 Pro Preview, Claude Opus 4.7, GPT-5.5).
  • SWE-bench Verified: No changes confirmed in window. Last known state (June 18): Claude Mythos 5 at 95.5%, Claude Fable 5 at 95.0%, Claude Opus 4.8 at 88.6%. Note: Mythos 5 is currently suspended under US export-control directive; this score reflects a pre-suspension submission.
  • Kimi K2.7 Code: Still no independent third-party SWE-bench or LiveCodeBench results. Vendor-reported proprietary benchmarks only. See Worth Watching.

Technical Discussions

Nothing cleared the quality bar this period. No Hacker News threads with score >200 for AI developer topics identified in the June 24–25 window. Simon Willison published one post on June 24 (converting MDN browser compatibility data to SQLite — not AI-relevant). No r/MachineLearning or r/LocalLlama threads with sufficient technical depth and confirmed June 24–25 recency.

Quick Hits

Worth Watching (Announced, Not Yet Shipped)

13

⚠️⚠️⚠️ imagen-3.0-capability-001 Retirement — **June 30 (5 days) — NO REPLACEMENT**

(Countdown updated — was 6 days)

imagen-3.0-capability-001 (Vertex AI mask-based editing: inpainting, background masking, semantic editing) retires June 30 with no replacement in the Gemini 3.x image stack. No migration path announced. Evaluate third-party alternatives (FLUX Fill, Stable Diffusion inpainting) now if you use mask-based editing workflows.

⚠️⚠️⚠️ Video Generation Models Shutdown — **June 30 (5 days)**

(Countdown updated — was 6 days)

Video generation models shutting down June 30. Recommended migration: Veo 3.1 preview model IDs. Check the deprecations page for specific model IDs affected.

⚠️⚠️ GPT-4.5 Retirement from ChatGPT — **June 27 (2 days)**

(Countdown updated — was 3 days)

GPT-4.5 removed from ChatGPT product surface June 27. API route retirement unconfirmed; audit any gpt-4.5 model identifiers in product or prompt code.

OpenAI Platform Changelog | Link: https://platform.openai.com/docs/changelog

⚠️⚠️ Kimi K2.7 Code Third-Party Benchmarks — **Still Pending**

(Status: no independent results as of June 25 — window extended to late June)

Weights released June 12, Arena entry June 17. As of June 25, no SWE-bench Verified, SWE-bench Pro, LiveCodeBench, or GPQA results from independent evaluators. Only vendor-reported proprietary benchmarks exist. Watch swebench.com and livecodebench.github.io.

⚠️⚠️ Gemini Code Assist for GitHub Full Shutdown — **~Mid-July (~20 days)**

(Countdown updated — was ~21 days)

New organizational installs blocked June 18. Full request serving ends approximately mid-July. Organizations using Gemini Code Assist for GitHub need an alternative.

⚠️ Grok V9-Medium — **API Release Still Pending**

(Status unchanged)

xAI deployed Grok V9-Medium to Tesla fleet and X users (1.5T parameters, 32B active). No API model ID, pricing, or public benchmarks as of June 25.

⚠️ Claude Mythos 5 — **Still Suspended**

(Status unchanged)

Claude Mythos 5 remains suspended under the US export-control directive issued June 12. Claude Fable 5 was reinstated June 18 after Anthropic deployed nationality-based access controls. Mythos 5 reinstatement timeline not announced. Migrate to claude-opus-4-8 for agentic workloads.

⚠️ Gemini 3.5 Pro — **GA Still Pending (Limited Vertex Enterprise Preview)**

(Status unchanged)

Expected: 2M token context, Deep Think reasoning mode. No GA date confirmed.

⚠️ Imagen 4 Model Shutdown — **August 17 (53 days)**

(Countdown updated — was 54 days)

Imagen 4 standard, ultra, and fast endpoints deprecated with August 17, 2026 shutdown date. Separate from the completed Imagen 3 shutdown.

Apple iOS 27 / macOS Golden Gate / Core AI GA — **Fall 2026 (September)**

(Status unchanged)

Includes Siri Extensions API, Core AI (replaces Core ML), Foundation Models multi-provider support (call Claude/Gemini via same Swift API as on-device model).

Apple Developer / WWDC 2026 | Link: https://developer.apple.com/ios/

⚠️ OpenAI Reusable Prompts / Evals Platform / Agent Builder Shutdown — **November 30 (158 days)**

(Countdown updated — was 159 days)

Export eval configs before October 31. Migrate Agent Builder to Agents SDK. Move prompt content from v1/prompts to application code.

Filtered from 30+ primary sources against a published quality rubric. No press releases, no fluff — only what changes what you build.