Every Major Model & Agent-Harness Release, August–September 2026

Models

Prices are standard (non-batch) API rates. Our cost calculator carries most of these, so you can model them against your own cache-hit rate, which is where most of the real difference between them shows up.

Gemini 3.8 Flash — the workhorse benchmark results kept pointing to

Released 2026-09-02 at $0.75 / $3.75 per MTok with a 1M context. This model doesn't have a video page of its own yet, but it keeps showing up in other people's tests. In IndyDevDan's five-benchmark ranking it placed second on Deep SWE (long-horizon software engineering), behind only GPT-6 Astra, and was a strong runner-up on the Omniscience hallucination benchmark. The trade-off is time. It takes more steps and more tokens to get there. What to do: it's the obvious candidate for the "workhorse" slot in a model stack. Route long, low-stakes tasks to it, and keep a frontier model for work where a wrong answer is expensive.

Grok 4.6 — and the pricing cliff at 200K

xAI's flagship since 2026-08-12, positioned for long-running agents, with a 500K context. The pricing detail to plan around: below 200K tokens a prompt costs $2 / $6 (cached input $0.50), but once a prompt reaches 200K, the whole request bills at $4 / $12, not just the tokens over the line. An agent that lets its context drift past 200K doubles its cost on every turn after that. On Automation Bench, which penalises guardrail violations, Grok 4.6 ranked near the top. It also runs underneath xAI's hosted agent product. See our new Grok Bot section.

Muse Spark 1.3 — Meta's agentic API model, with a cheap tier that trains on your data

Released 2026-09-02 on Meta's API (and in Muse Code) as muse-spark-1.3: $1.25 input / $0.15 cached / $4.25 output per MTok, 1M context. Meta says it uses about 20% fewer tool calls and 25% fewer tokens than 1.2 on its internal comparisons, and is tuned for longer multi-step work. There is also a muse-spark-1.3-contributor variant at $0.10 / $0.002 / $0.20, which Meta labels "used to improve our products". What to do: treat the contributor tier as a public channel. It's fine for open-source code or synthetic tests, but never for client data, credentials, or anything under NDA.

DeepSeek V4.1 Flash — the old model names now route to it

Released 2026-09-10 as deepseek-flash: a 552B-parameter MoE with native vision and a 1M context. Peak: $0.30 / $1.20. Off-peak: exactly half. Cache hits: $0.006 peak. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday. The migration detail: V4 Flash and V4 Flash Vision Exp are retired. The names deepseek-v4-flash and deepseek-v4-flash-vision-exp still work, but they route to and bill as V4.1 Flash. If you pinned the old name for reproducibility, you're already on a different model. Measured on real builds, it cost 4–6× less than Astra but took 3–5× longer.

GLM-5.3-Flash and FlashX — a fast open-weights option

glm-5.3-flash (launched 2026-08-26, MIT licence, weights on Hugging Face) is the first natively multimodal GLM-5 model: 320B total parameters, 18B active, 1M context, 128K max output. glm-5.3-flashx followed on 2026-09-18 at up to 200 tokens/second. Z.ai's docs say FlashX isn't on the GLM Coding Plan yet, while Flash is. In the benchmark roundups it's a strong budget option for coding, but weak on hallucination, so pair it with verification, not trust. Z.ai's docs page doesn't list per-token prices, so check your provider before routing volume to it.

Harnesses: what changed in how agents load and run