Mixtral 8x22B vs Qwen 3 235B-A22B — MoE Compared
Last updated 2026-06-26
Two open-weight mixture-of-experts heavyweights, a year apart. We compare architecture, benchmarks, VRAM and cost to tell you which one to actually run.
By Mohamed Meguedmi · 9 min read
Key Takeaways
- Qwen3-235B-A22B (Apr 2025) clearly outperforms Mixtral 8x22B (Apr 2024) on reasoning, math and coding — typically a 10-point MMLU gap and a far larger one on HumanEval and AIME.
- Active parameters favor Qwen3 for speed: Mixtral fires 39B params per token, Qwen3 only 22B, so Qwen3 is often the faster generator despite a 235B total footprint.
- Both are Apache 2.0 and free for commercial use. Both fit a 4×RTX 3090 or dual A100 80GB node at Q4_K_M (~80 GB vs ~133 GB).
- Qwen3 adds a hybrid thinking mode and 128k context (vs Mixtral's 64k). Mixtral stays lighter to host and dead simple to deploy.
- Verdict: pick Qwen3-235B-A22B for quality; keep Mixtral 8x22B only when VRAM or operational simplicity is a hard constraint.
The Two Models at a Glance
Both models are sparse mixture-of-experts (MoE) transformers, but they belong to different generations. Mixtral 8x22B, released by Mistral AI in April 2024, was the open-weight community's first taste of a frontier-class MoE you could actually download. Qwen3-235B-A22B, shipped by Alibaba a year later in April 2025, represents the next wave: more, smaller experts, a hybrid reasoning mode, and a longer context window.
| Spec | Mixtral 8x22B Instruct v0.1 | Qwen3-235B-A22B |
|---|---|---|
| Vendor | Mistral AI | Alibaba (Qwen) |
| Released | April 2024 | April 2025 |
| Total parameters | 141B | 235B |
| Active params / token | ~39B | ~22B |
| Experts (total / active) | 8 / 2 | 128 / 8 |
| Context window | 64k | 128k (up to 262k extended) |
| Hybrid thinking mode | No | Yes |
| License | Apache 2.0 | Apache 2.0 |
The naming conventions hint at the design philosophy. Mixtral's "8x22B" advertises 8 experts of roughly 22B each; Qwen3's "235B-A22B" tells you the total size (235B) and the active size per token (22B). That A22B is the number that matters most for inference latency.
Architecture — How the MoE Designs Differ
A mixture-of-experts layer replaces a single dense feed-forward block with many parallel "expert" blocks plus a router that picks a few per token. Only the chosen experts run, so you get the knowledge capacity of a huge model at the compute cost of a much smaller one.
Mixtral uses a coarse-grained design: 8 large experts, top-2 routing. Every token activates 2 of 8 experts, which works out to ~39B active parameters once you include shared attention layers. It's simple and battle-tested.
Qwen3 uses a fine-grained design: 128 small experts, top-8 routing. Spreading the same job across many smaller specialists improves routing granularity and parameter efficiency — a trend established by DeepSeek-V2/V3 and adopted across the 2025 generation. Despite holding 235B total parameters, Qwen3 keeps only ~22B active, fewer than Mixtral. That is the counterintuitive headline: the bigger model is the leaner one at runtime.
More total parameters means more stored knowledge; fewer active parameters means faster tokens. Qwen3 wins on both axes — at the price of a larger memory footprint you must keep resident.
Qwen3 also introduces a hybrid thinking mode: a single checkpoint that can either answer directly or emit an explicit chain-of-thought block before responding, toggled at inference time. Mixtral has no equivalent; it is a straightforward instruct model.
Benchmarks — Reasoning, Coding, Knowledge
Published figures from the official model cards and independent aggregators tell a consistent story. The gap is not subtle. The numbers below are representative published scores; Qwen3's reasoning-heavy results reflect its thinking mode enabled.
| Benchmark | Mixtral 8x22B Instruct | Qwen3-235B-A22B |
|---|---|---|
| MMLU | ~77.8 | ~87.8 |
| MMLU-Pro | ~51 | ~68 |
| GSM8K (grade-school math) | ~88 | ~94+ |
| MATH | ~41 | ~71 |
| HumanEval (coding) | ~45 | ~85+ |
| AIME 2024 (competition math) | low single digits | ~65-85 (thinking) |
On general knowledge (MMLU) the gap is roughly 10 points. On anything requiring multi-step reasoning — competition math, hard coding, MMLU-Pro — it widens into a chasm. AIME in particular is brutal for Mixtral, which predates the reasoning-tuned era; Qwen3's thinking mode was built for exactly this. For an apples-to-apples view across more suites, see our benchmarks hub, and cross-check raw scores on Artificial Analysis.
The honest caveat: Mixtral remains a competent generalist for drafting, summarization, classification and retrieval-augmented chat. If your workload is not reasoning-bound, the day-to-day quality difference shrinks. But there is no task on which Mixtral 8x22B beats Qwen3-235B-A22B on quality.
Hardware & Quantization Requirements
This is where Mixtral fights back. Total parameters drive memory footprint, and 235B is a lot to keep resident. Approximate GGUF/weights sizes:
| Quantization | Mixtral 8x22B (~141B) | Qwen3-235B-A22B (~235B) | Example host |
|---|---|---|---|
| FP16 | ~282 GB | ~470 GB | Multi-GPU server (8×A100/H100) |
| Q8_0 | ~141 GB | ~235 GB | 4×A100 80GB |
| Q4_K_M | ~80 GB | ~133 GB | 2×A100 80GB / 4×RTX 3090 |
| Q3_K_M | ~58 GB | ~100 GB | 3×RTX 3090 / dual A6000 |
Neither model is a single-consumer-GPU proposition at usable quality. But notice the asymmetry: at Q4_K_M, Mixtral fits comfortably in 80 GB, while Qwen3 needs ~133 GB plus KV-cache headroom for its longer context. If your ceiling is a single 80 GB card plus offload, Mixtral runs cleanly where Qwen3 forces aggressive quantization or a second GPU. You can model your own footprint and token costs with our cost calculator.
Inference Speed & Cost
Because tokens-per-second tracks active parameters, not total, Qwen3's 22B active vs Mixtral's 39B active means Qwen3 typically generates faster per token on comparable hardware — assuming the full weights fit in memory. The trade is throughput vs residency: Qwen3 is the quicker generator but the heavier tenant.
On hosted APIs the price signal is stark. Mixtral 8x22B Instruct lists around $2 per million input tokens on typical providers, while Qwen3-235B-A22B is offered as low as ~$0.09–$0.20 per million input tokens thanks to its small active footprint and intense 2025 price competition. The newer, better model is also the cheaper one to call — an unusual but defining feature of this matchup.
For self-hosters, the calculus is electricity and amortized GPU cost per generated token. Qwen3's lower active-parameter count generally wins there too, provided you can afford the VRAM to hold it resident in the first place.
Which Should You Run?
Match the model to the constraint that actually binds you:
- You want the best open-weight quality you can self-host: Qwen3-235B-A22B, no contest. Reasoning, coding, math and long-context all favor it.
- You're VRAM-limited to a single 80 GB GPU (plus modest offload): Mixtral 8x22B at Q4_K_M is the pragmatic pick.
- You need agentic / tool-use workflows: Qwen3's thinking mode and stronger function-calling make it the better agent backbone.
- You value deployment simplicity and predictable latency: Mixtral is older, simpler, and extremely well-supported across Ollama, vLLM and llama.cpp.
- You process long documents (>64k tokens): Qwen3's 128k window is mandatory; Mixtral can't reach it.
Browse both — and lighter alternatives like Qwen3-30B-A3B or Mixtral 8x7B — in our model catalog if neither full-size MoE fits your budget. All comparison data on this page is available through the BestLLMfor public API (CC BY 4.0) and our open-source MCP server, so you can pull these benchmark and pricing numbers directly into your own tooling.
Conclusion — The Verdict
This is a generational mismatch as much as a head-to-head. Mixtral 8x22B was a landmark in 2024 and remains a solid, lean generalist. But Qwen3-235B-A22B is simply the stronger model on nearly every axis that matters in 2026 — quality, speed-per-token, context length, and even hosted price — and it costs you only in raw VRAM footprint.
| Use case | Winner |
|---|---|
| Best overall quality | Qwen3-235B-A22B |
| Coding & math | Qwen3-235B-A22B |
| Reasoning / agents | Qwen3-235B-A22B |
| Long context (>64k) | Qwen3-235B-A22B |
| Lowest VRAM to host | Mixtral 8x22B |
| Simplest deployment | Mixtral 8x22B |
| Tokens-per-second (resident) | Qwen3-235B-A22B |
Bottom line: default to Qwen3-235B-A22B. Reach for Mixtral 8x22B only when your hardware ceiling or operational simplicity forces the issue.
Frequently Asked Questions
Is Qwen3-235B-A22B better than Mixtral 8x22B?
Yes, on quality. Qwen3 leads by roughly 10 points on MMLU and by much larger margins on coding (HumanEval), competition math (AIME) and reasoning (MMLU-Pro). Mixtral remains a capable generalist but does not beat Qwen3 on any standard benchmark.
Can I run either model on a single consumer GPU?
Not at usable quality. Even at Q4_K_M, Mixtral needs ~80 GB and Qwen3 ~133 GB. Realistic hosts are 2-4×RTX 3090/A100-class GPUs. For a single 24 GB card, look at smaller variants such as Qwen3-30B-A3B instead.
Which model generates tokens faster?
Qwen3-235B-A22B, generally. Inference speed tracks active parameters — 22B for Qwen3 versus 39B for Mixtral — so Qwen3 is the quicker generator when its full weights are resident in memory.
Are both models free for commercial use?
Yes. Both Mixtral 8x22B and Qwen3-235B-A22B ship under the Apache 2.0 license, which permits commercial use, modification and redistribution.
How much context can each handle?
Mixtral 8x22B supports 64k tokens. Qwen3-235B-A22B supports 128k natively and can be extended toward 262k. For long-document workloads, Qwen3 is the only viable option of the two.
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