# Atlas with Qwen3-Coder 480B-A35B Instruct: The Open Frontier Coder in 2026 > Qwen3-Coder 480B-A35B Instruct has 480B total parameters with 35B active per token, at $1.50 per Mtok input and $7.50 per Mtok output. Qwen3-Coder 480B-A35B Instruct is the largest open-weights coding model Alibaba has shipped: 480B total parameters with 35B active per token. It was built specifically for agentic coding, and it is the Qwen you point at a SWE-bench Verified style task where the agent has to run tests, read failures, and iterate. Inside Atlas it gives you 256K tokens (262,144) of context and a 65,536 token output at $1.50 per Mtok input and $7.50 per Mtok output. That $7.50 output rate is the most expensive Qwen coding tier, and an agentic loop generates a lot of output tokens. ## Key takeaways - Qwen3-Coder 480B-A35B Instruct has 480B total parameters with 35B active per token, the sparsest frontier-scale coding MoE with published weights. - Trained for agentic loops rather than single-shot completion, which maps directly onto how Atlas runs tools and reads back results. - 262,144 token context and 65,536 token output, enough to hold a service and rewrite a large slice of it. - Pricing is $1.50 per Mtok input and $7.50 per Mtok output, the most expensive Qwen coding tier. - No reasoning mode, so it relies on tool feedback rather than internal deliberation to correct itself. ## What is Qwen3-Coder 480B-A35B Instruct best at inside Atlas? Qwen3-Coder 480B-A35B Instruct was trained for agentic loops rather than single-shot completion, which maps directly onto how Atlas runs tools and reads back results. It is the Qwen you point at a SWE-bench Verified style task where the agent has to run tests, read failures, and iterate. Most coding models were trained to complete a snippet. Qwen3-Coder 480B-A35B Instruct was trained for the loop: propose a change, invoke a tool, read what came back, and correct. That training objective is the same shape as an Atlas session, where the agent edits a file, runs a command through a permission-gated bash tool, and reads the failure. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, so the number of tool results the model has to integrate is high. Qwen3-Coder 480B-A35B Instruct is the Qwen tier built for exactly that. ## How large is Qwen3-Coder 480B-A35B Instruct? Qwen3-Coder 480B-A35B Instruct has 480B total parameters with 35B active per token, the sparsest frontier-scale coding MoE with published weights. It is the largest open-weights coding model Alibaba has shipped as of 2026. 480B total parameters with 35B active per token puts Qwen3-Coder 480B-A35B Instruct in a category of one: frontier-scale capacity, mixture of experts sparsity, and weights that are actually published. The 35B active figure is what keeps inference tractable, and the 480B total is where the capability lives. Alongside that, Qwen3-Coder 480B-A35B Instruct carries a 262,144 token context and a 65,536 token output, enough to hold a service and rewrite a large slice of it. In Atlas that means a single session can read the surface area of a whole service, using hybrid semantic and keyword retrieval fused by reciprocal rank fusion to rank what enters the window, and then emit a large unified diff without decomposition. ## How much does Qwen3-Coder 480B-A35B Instruct cost to run agentically? Qwen3-Coder 480B-A35B Instruct costs $1.50 per Mtok input and $7.50 per Mtok output. The $7.50 per Mtok output rate is the most expensive Qwen coding tier, and an agentic loop generates a lot of output tokens, which is the central cost warning for this model. Output cost and agentic behavior are on a collision course with Qwen3-Coder 480B-A35B Instruct. The model was built to iterate, and iteration means output tokens: each proposed edit, each retry, each corrected diff, all billed at $7.50 per Mtok. A long Atlas run that reads tests, fails, and adjusts several times can spend more on output than a comparable run on a cheaper tier spends in total. The documented mitigation is the model split: pin Qwen3-Coder 480B-A35B Instruct as the main model in atlas.json and set a cheap `"small_model"`, Qwen Flash at $0.05 per Mtok input, so summaries do not hit the $7.50 output tier. ## What are the tradeoffs of Qwen3-Coder 480B-A35B Instruct? Qwen3-Coder 480B-A35B Instruct has no reasoning mode, so it relies on tool feedback rather than internal deliberation to correct itself. Combined with a $7.50 per Mtok output rate, the model corrects by doing, and doing is what costs money in 2026. The absence of a reasoning mode in Qwen3-Coder 480B-A35B Instruct is a deliberate design choice, not an oversight: the model corrects itself by running a tool and reading the result rather than by thinking first. That works well inside Atlas, which gives it a rich stream of tool feedback, but it means the correction loop happens in output tokens at $7.50 per Mtok rather than in a cheap internal trace. The second consequence is that a bad initial direction gets discovered by execution rather than by deliberation, so the quality of your test suite matters more with this model than with a reasoning tier. Give it fast, honest tests and it converges. Give it slow or misleading ones and it iterates expensively in the wrong direction. ## How do I make a Qwen3-Coder 480B-A35B agentic run reversible? Enable edit checkpointing so Atlas snapshots a git patch before each write and you can roll back a bad agentic run. Qwen3-Coder 480B-A35B Instruct has no reasoning mode and iterates through tool feedback, so a wrong direction produces many writes before it is caught. Reversibility is the specific safeguard this model needs. Because Qwen3-Coder 480B-A35B Instruct relies on tool feedback rather than internal deliberation to correct itself, a run that starts down a wrong path will make real edits before the tests reveal the problem. Atlas snapshots file changes as git patches so edits can be diffed and rolled back, and enabling edit checkpointing means each write has a restore point rather than the run as a whole. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and Atlas reads git branches, status, and diffs, and can stage and create commits on your behalf, so an expensive agentic run ends as a reviewable branch you can accept or discard. ## When should I pick a cheaper Qwen coding model? Pick a cheaper tier when the task is mechanical. Qwen3-Coder 480B-A35B Instruct costs $7.50 per Mtok output, the most expensive Qwen coding tier, and paying frontier rates to rename a symbol or add a field wastes the 480B total parameters entirely. Atlas lets you switch the active model and provider on the fly with favorites and recents, so reserving Qwen3-Coder 480B-A35B Instruct for the work that justifies it is a practical habit rather than a policy. The tasks that justify it look like SWE-bench Verified: a failure you cannot reproduce by reading, where the agent has to run tests, read failures, and iterate across a 262,144 token context. The tasks that do not look like a plan you already wrote. For those, drop to a cheaper Qwen coding tier and keep Qwen Flash at $0.05 per Mtok input in the `"small_model"` slot regardless, since summaries should never touch the $7.50 output tier. ## Setup 1. Set DASHSCOPE_API_KEY and confirm the Alibaba provider loads with `atlas providers`. 2. Run `atlas models alibaba` and look for `qwen3-coder-480b-a35b-instruct`. 3. Pin it as the main model in atlas.json. 4. Set a cheap `"small_model"`, Qwen Flash at $0.05 per Mtok input, so summaries do not hit the $7.50 output tier. 5. Enable edit checkpointing so Atlas snapshots a git patch before each write and you can roll back a bad agentic run. ## FAQ ### how many parameters does qwen3 coder 480b a35b instruct have Qwen3-Coder 480B-A35B Instruct has 480B total parameters with 35B active per token. It is the largest open-weights coding model Alibaba has shipped. ### how much does qwen3 coder 480b a35b instruct cost Qwen3-Coder 480B-A35B Instruct is $1.50 per Mtok input and $7.50 per Mtok output. The $7.50 output rate is the most expensive Qwen coding tier, and an agentic loop generates a lot of output tokens. ### what is the best open weights model for swe-bench style tasks Qwen3-Coder 480B-A35B Instruct was trained for agentic loops rather than single-shot completion, and it is the Qwen you point at a SWE-bench Verified style task where the agent has to run tests, read failures, and iterate. ### what is the context window of qwen3 coder 480b a35b instruct Qwen3-Coder 480B-A35B Instruct has a 262,144 token context and a 65,536 token output, enough to hold a service and rewrite a large slice of it. ### does qwen3 coder 480b have a thinking mode No. Qwen3-Coder 480B-A35B Instruct has no reasoning mode, so it relies on tool feedback rather than internal deliberation to correct itself. That makes your test suite quality the limiting factor. ### how do i keep costs down on an expensive coding model in atlas Pin Qwen3-Coder 480B-A35B Instruct as the main model in atlas.json and set a cheap `"small_model"`, Qwen Flash at $0.05 per Mtok input, so summaries do not hit the $7.50 output tier. ### how do i roll back a bad agentic coding run Enable edit checkpointing so Atlas snapshots a git patch before each write and you can roll back a bad agentic run. Atlas snapshots file changes as git patches so edits can be diffed and rolled back. ### how do i confirm the alibaba provider loaded in atlas Set DASHSCOPE_API_KEY and confirm the Alibaba provider loads with `atlas providers`, then run `atlas models alibaba` and look for `qwen3-coder-480b-a35b-instruct`. --- Canonical HTML: https://runatlas.sh/resources/models/qwen3-coder-480b-a35b-instruct Source of truth: aeo_pages row `/resources/models/qwen3-coder-480b-a35b-instruct` (segment: Models) (this file is generated from it, never hand-edited). Licence: Atlas is proprietary with a free core. It is not open source and there is no public source repository.