Meta today , a coding-focused update to its Muse Spark family of frontier models — a one-two punch that puts the company in direct competition with Anthropic's Claude Code, OpenAI's Codex, and the growing field of agentic coding harnesses that have rapidly become the primary way many professional developers ship software.
"Releasing Muse Code in beta today," Meta CEO Mark Zuckerberg wrote in a , it vaulted Meta back into the top five on frontier reasoning and vision benchmarks — but trailed on the agentic coding evaluations that matter most to this market, scoring 77.4 on SWE-Bench Verified against Claude Opus 4.6's 80.8 and Gemini 3.1 Pro's 80.6, and lagging well behind GPT-5.4 on GDPval's measure of long-horizon work tasks.
Four months later, a coding-specialized checkpoint paired with a purpose-built harness reads as Meta's direct answer to that gap.
Two training details stand out. First, Meta co-trained the model with Muse Code itself, using rejection-sampled harness trajectories and recipe optimizations for goals, context compaction, and sub-agents — meaning the model was explicitly tuned to perform best inside this particular tool. That mirrors an industry-wide shift away from treating models and harnesses as separable products.
Second, Meta used a self-improvement loop: Muse Spark 1.1 generated challenging coding environments and instruction-following templates, then graded candidate solutions against those requirements, producing a scalable training dataset for its successor. Meta credits the loop with making 1.2 measurably better at following complex instructions.
Meta published benchmark charts comparing Muse Spark 1.2 against other coding models on Terminal-Bench 2.1, DeepSWE 1.1, and an internal Meta coding benchmark, pointing readers to a separate methodology report for details — though the company did not headline specific scores in the announcement itself, a notable omission in a field where rivals trumpet leaderboard placement.
The company's most striking demonstration is a long-horizon case study: Meta pointed Muse Spark 1.2 at GPU kernel optimization and let it run for more than 1,000 tool calls over up to 24 hours on NVIDIA Hopper hardware.
Working in Triton and barred from simply wrapping existing third-party kernel libraries, the agent wrote, compiled, and profiled its way to what Meta calls "substantial improvements" over baseline implementations of KDA and MLA kernels — including genuinely non-obvious optimizations like re-centering gated cumulative decay at a chunk midpoint.
"It kept finding substantial improvements well beyond the initial exploration phase," Zuckerberg wrote. Sustained improvement over a 24-hour autonomous run, if it holds up outside Meta's demos, addresses one of the most persistent criticisms of coding agents: that they plateau or drift once past their initial burst of progress.
Your data for a discount?
The pricing structure may be the most consequential — and most scrutinized — part of the launch. Meta is offering Muse Spark 1.2 through its
MiMo-V2.5 Flash
$0.10
$0.30
$0.40
deepseek-v4-pro
$0.435
$0.87
$1.305
MiniMax-M3
$0.30
$1.20
$1.50
Gemini 3.1 Flash-Lite
$0.25
$1.50
$1.75
Gemini 3.5 Flash-Lite
$0.30
$2.50
$2.80
MiMo-V2.5 Pro (≤256K)
$1.00
$3.00
$4.00
GLM-5.2
$1.40
$4.40
$5.80
MiMo-V2.5 Pro (>256K)
$2.00
$6.00
$8.00
Gemini 3.6 Flash
$1.50
$7.50
$9.00
Gemini 3.1 Pro Preview (≤200K)
$2.00
$12.00
$14.00
GPT-5.4
$2.50
$15.00
$17.50
Gemini 3.1 Pro Preview (>200K)
$4.00
$18.00
$22.00
GPT-5.5
$5.00
$30.00
$35.00
Sakana Fugu Ultra (≤272K)
$5.00
$30.00
$35.00
Claude Fable 5 / Claude Mythos 5
$10.00
$50.00
$60.00
This is the tier Zuckerberg is steering new users toward: "It's easy and low-cost to get started," he wrote. "Install Muse Code with one line and you can start on our contributor tier."
In VentureBeat's own testing on a Mac mini, the one-line installer worked as advertised — a 97 MB download and a sign-in — but the agent stopped short of running anything, reporting that no models were visible and that payment was "required to finish setting up your account."
In other words, even the heavily discounted contributor tier requires a payment method on file before Muse Code will do any work: low-cost is accurate, but free is not.
Meta frames the contributor tier as lowering the barrier for prototyping and experimentation "where training on your data is acceptable."
But it also means the default on-ramp for Muse Code sends developers' code and prompts into Meta's training pipeline — a tradeoff enterprises with proprietary codebases will need to consciously opt out of by moving to standard pricing.
The contributor tier also carries much tighter rate limits (60 requests per minute versus 3,000), a clear signal it's aimed at individuals and small experiments rather than production workloads.
The approach is classically Meta: subsidize access, harvest data at scale, and use it to close the gap with the frontier. Zuckerberg made no secret of the ambition, calling Muse Spark 1.2 "our next step as we push toward frontier, with larger, more capable models on the way."
However, for developers and enterprises who want or are required legally to keep their code secure, the tradeoff may not be one they're willing or able to make.
No Llama in sight
What today's announcement conspicuously lacks is any mention of open source — a striking omission from the company that spent three years positioning itself as the standard-bearer of open AI.
From the original LLaMA's debut in February 2023 — whose weights famously leaked onto 4chan within weeks, inadvertently kickstarting the movement to run capable models on consumer hardware — through Llama 2's commercially usable license, the coding-specialized Code Llama, and the 405-billion-parameter Llama 3.1, which Zuckerberg launched in July 2024 with a manifesto titled ", averaging about a million downloads a day, with self-hosting offering enterprises cost reductions VentureBeat has previously reported at as much as 88% versus proprietary API providers.
Then came the unraveling. Llama 4 debuted in April 2025 to — "the most powerful model that meta has released," in Wang's words — as Meta's first proprietary model: — but four months on, today's release does nothing to advance that promise: no weights, no license, and neither the blog post nor Zuckerberg's thread so much as uses the word "open."
The reversal is all the sharper because Meta's rivals have been moving in the opposite direction. OpenAI released its ; Google's Gemini CLI harness is likewise Apache-licensed.
With Muse Code, Meta lands closest to the posture of Anthropic — whose Claude Code remains proprietary — while the company that once argued open source was the path forward now asks developers to pay per token for a model they cannot inspect, or to subsidize that access with their own data.
Seen in that light, the contributor tier reads as the successor to the Llama strategy itself: the ecosystem flywheel is no longer free weights in exchange for mindshare, but cheap tokens in exchange for training data.
Why it matters
Terminal coding agents have become the fastest-growing surface in enterprise AI, and until today the category has effectively been a two-horse race between Anthropic and OpenAI, with Google and a crowd of startups in pursuit.
Meta's entry brings a genuinely different architecture (persistent background agents, an append-only local event log), a credible long-horizon demo, and an aggressive pricing wedge.
The open questions are the ones benchmarks charts can't answer: whether Muse Spark 1.2 actually matches Claude and GPT-class models on real-world repositories, whether developers trust Meta with their code, and whether the contributor tier's discount is enough to make them stop asking. Muse Code is available in beta today; Muse Spark 1.2 is live in the Meta Model API with expanded global access.
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