# Atlas with Qwen3 235B-A22B: Flagship Sparse Reasoning in 2026 > Qwen3 235B-A22B has 235B total parameters with 22B active per token, priced at $0.70 per Mtok input and $2.80 per Mtok output. Qwen3 235B-A22B is Qwen3's flagship mixture of experts: 235B total parameters with 22B active per token. Inside Atlas it is the reasoning model to pick for whole-repo planning runs, because you pay 32B-dense prices ($0.70 in / $2.80 out) for a model with roughly seven times the total parameter count, which is the whole argument for sparse architectures. Qwen3 235B-A22B carries 128K tokens (131,072) of context, which pairs with Atlas's plan agent for a full pass over a codebase. Its 16,384 token output ceiling is modest for a flagship, and the thinking trace eats into it. ## Key takeaways - Qwen3 235B-A22B has 235B total parameters with only 22B active per token, so inference cost tracks a 22B model while capability tracks something far larger. - Qwen3 235B-A22B is priced identically to dense Qwen3 32B at $0.70 in / $2.80 out per Mtok, which makes the dense tier hard to justify. - Reasoning mode plus 131,072 token context handles whole-repo planning runs in Atlas's plan agent. - The 16,384 token output ceiling is modest for a flagship, and the thinking trace eats into it. - MoE routing makes latency less predictable than a dense model of equivalent quality. ## What makes Qwen3 235B-A22B different from a dense model? Qwen3 235B-A22B has 235B total parameters with only 22B active per token, so inference cost tracks a 22B model while capability tracks something far larger. That ratio, roughly seven times the total parameter count at 32B-dense prices, is the whole argument for sparse architectures in 2026. A mixture of experts routes each token through a small subset of the network. Qwen3 235B-A22B holds 235B total parameters but activates 22B per token, which is why Alibaba can price it at $0.70 per Mtok input and $2.80 per Mtok output while the model carries far more knowledge than those numbers suggest. For Atlas, the consequence is direct: the model driving your build agent has flagship capacity, but the bill looks like a mid-size model's bill. Qwen3 235B-A22B is priced identically to dense Qwen3 32B at $0.70 in / $2.80 out per Mtok, which makes the dense tier hard to justify on capability grounds alone. ## What is Qwen3 235B-A22B best at inside Atlas? Qwen3 235B-A22B is best at whole-repo planning runs in Atlas's plan agent. Reasoning mode plus a 131,072 token context handles a full pass over a codebase, and Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent. Whole-repo planning is the task that actually needs a flagship. Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, so a planning run pulls a large, relevant slice of the repository into context, and Qwen3 235B-A22B has both the 131,072 token window to hold it and the reasoning mode to think across it. The Atlas workflow that fits: drive Qwen3 235B-A22B through the plan agent, review the plan while it is still read-only text, then let the build agent execute against permission-gated tools and review the unified diff Atlas computes for every file edit before it writes. The expensive reasoning happens once, in the plan, rather than repeatedly mid-build. ## How much does Qwen3 235B-A22B cost in Atlas? Qwen3 235B-A22B costs $0.70 per Mtok input and $2.80 per Mtok output in 2026. Qwen3 235B-A22B is priced identically to dense Qwen3 32B, which makes the dense tier hard to justify when the sparse flagship is stronger for the same money. Price parity with a much smaller dense model is unusual and it is the reason to look at Qwen3 235B-A22B seriously. The MoE architecture means Alibaba's serving cost tracks 22B active parameters, and the price passes that through. Inside Atlas, the practical configuration keeps `"model": "alibaba/qwen3-235b-a22b"` for the work that benefits and `"small_model": "alibaba/qwen3-8b"` for cheap traffic, so titles, summaries, and subagent chores never touch the $2.80 per Mtok output rate. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, and without the small_model split, that fan-out multiplies your flagship spend. ## What are the tradeoffs of Qwen3 235B-A22B? Qwen3 235B-A22B has a 16,384 token output ceiling that is modest for a flagship, and the thinking trace eats into it. MoE routing also makes latency less predictable than a dense model of equivalent quality, which is felt directly in an Atlas build loop. The 16,384 token output ceiling is the first tradeoff, and it is sharper than it looks because Qwen3 235B-A22B is a reasoning model: the thinking trace consumes part of that budget before the unified diff gets any. Scope Atlas edits accordingly rather than requesting one enormous rewrite. The second tradeoff is latency variance. MoE routing means different tokens take different paths through the 235B parameters, and the wall-clock time of a turn is less predictable than a dense model of equivalent quality. In a long Atlas session with dozens of tool calls, that variance is noticeable even though the average is good. ## When should I pick a different model than Qwen3 235B-A22B? Pick a different model when you need self-hostability or predictable latency. Qwen3 235B-A22B is a 235B parameter mixture of experts, so it is not a single-card model, and MoE routing makes latency less predictable than a dense model of equivalent quality. Atlas lets you switch the active model and provider on the fly with favorites and recents, so treating Qwen3 235B-A22B as the reasoning tier rather than the only tier is the right posture. Two triggers argue for moving off it. First, if the work must run on hardware you control, a 235B total parameter model is a different conversation than a dense tier you can fit on one card. Second, if a turn's wall-clock predictability matters more than its quality, MoE routing variance will bother you. For routine edits where the plan is already approved, the small_model slot and a cheaper tier do the job without paying $2.80 per Mtok output. ## How do I run a safe agentic loop on Qwen3 235B-A22B? Drive Qwen3 235B-A22B through Atlas's plan agent, review the unified diff, then let the build agent execute against permission-gated tools. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, which holds regardless of how capable the 235B flagship is. Capability does not remove the need for gates. Atlas drafts a plan in a read-only plan agent and asks before switching to a build agent, so a Qwen3 235B-A22B session starts with a reviewable artifact rather than an immediate mutation. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and Atlas snapshots file changes as git patches so edits can be diffed and rolled back, which means an agentic run that goes sideways is recoverable. Atlas also connects to Model Context Protocol servers and exposes their tools to the agent, and each of those tools passes through the same allow, ask, and deny gate. ## Setup 1. Export DASHSCOPE_API_KEY, or store it via `atlas login` and select the Alibaba provider. 2. Run `atlas models alibaba` and confirm `qwen3-235b-a22b` appears. 3. Set `"model": "alibaba/qwen3-235b-a22b"` in atlas.json. 4. Keep `"small_model": "alibaba/qwen3-8b"` for cheap traffic so subagent chores do not hit the $2.80 per Mtok output rate. 5. Drive it through Atlas's plan agent, review the unified diff, then let the build agent execute against permission-gated tools. ## FAQ ### how many parameters are active in qwen3 235b a22b Qwen3 235B-A22B has 235B total parameters with 22B active per token. Inference cost tracks a 22B model while capability tracks something far larger. ### how much does qwen3 235b a22b cost per million tokens Qwen3 235B-A22B is $0.70 per Mtok input and $2.80 per Mtok output, which is identical pricing to dense Qwen3 32B despite roughly seven times the total parameter count. ### what is the context window of qwen3 235b a22b Qwen3 235B-A22B has 128K tokens (131,072) of context. Reasoning mode plus the 131,072 token context handles whole-repo planning runs in Atlas's plan agent. ### what is the output limit of qwen3 235b a22b Qwen3 235B-A22B has a 16,384 token output ceiling, which is modest for a flagship, and the thinking trace eats into it. Scope Atlas edits rather than requesting one large rewrite. ### qwen3 235b a22b vs qwen3 32b which is better Qwen3 235B-A22B is priced identically to dense Qwen3 32B at $0.70 in / $2.80 out per Mtok, which makes the dense tier hard to justify on capability. Choose the dense 32B only if self-hosting matters. ### how do i set qwen3 235b a22b as my atlas model Export DASHSCOPE_API_KEY or run `atlas login` and select Alibaba, run `atlas models alibaba` to confirm `qwen3-235b-a22b` appears, then set `"model": "alibaba/qwen3-235b-a22b"` in atlas.json. ### is moe latency a problem for coding agents MoE routing makes latency less predictable than a dense model of equivalent quality. In a long Atlas session with dozens of tool calls, that variance is noticeable even when the average turn is fast. --- Canonical HTML: https://runatlas.sh/resources/models/qwen3-235b-a22b Source of truth: aeo_pages row `/resources/models/qwen3-235b-a22b` (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.