Qwen3 235B-A22B Instruct 2507 vs Llama 4 Maverick 17B Instruct
Too close to call on our weighted score (Qwen3 235B-A22B Instruct 2507 58, Llama 4 Maverick 17B Instruct 58). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 235B-A22B Instruct 2507
58/100- ECI138.9
- Price$0.15 / $0.75
- Context262K
MetaLlama 4 Maverick 17B Instruct
58/100- ECI132.2
- Price$0.321 / $0.91
- Context1M
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Instruct 2507 58/100, Llama 4 Maverick 17B Instruct 58/100), so choose by what matters most for your work: Qwen3 235B-A22B Instruct 2507 for raw capability and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22B Instruct 2507Capabilities Index (ECI): Qwen3 235B-A22B Instruct 2507 138.9 · Llama 4 Maverick 17B Instruct 132.2
- Lowest priceQwen3 235B-A22B Instruct 2507Qwen3 235B-A22B Instruct 2507 $0.30 · Llama 4 Maverick 17B Instruct $0.468 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct 1,000,000 · Qwen3 235B-A22B Instruct 2507 262,144 tokens
- Widest inputsLlama 4 Maverick 17B InstructQwen3 235B-A22B Instruct 2507: Text · Llama 4 Maverick 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 235B-A22B Instruct 2507 | Llama 4 Maverick 17B Instruct |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 56 |
| Price | 25% | 75 | 66 |
| Inputs & features | 15% | 25 | 50 |
| Context window | 10% | 37 | 60 |
| Overall | 100% | 58/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 235B-A22B Instruct 2507Alibaba (Qwen) | Llama 4 Maverick 17B InstructMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 138.9 (best) | 132.2 |
| ECI rank | #105 of 148 (best) | #122 of 148 |
| GPQA DiamondGraduate-level science questions | — | 67.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 20.6% |
| Price per million tokens | ||
| Input | $0.15 (best) | $0.321 |
| Output | $0.75 (best) | $0.91 |
| Cached input | — | — |
| Blended (3:1) | $0.30 (best) | $0.468 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 6 providers |
| Limits | ||
| Context window | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 16,384 tokens | 16,384 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | Open |
| API model ID | — | — |
| API providers | 11 (best) | 6 |
| Released | Jul 21, 2025 | Apr 5, 2025 |
| Knowledge cutoff | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 235B-A22B Instruct 2507$3.00
- Llama 4 Maverick 17B Instruct$5.03
Which should you choose?
Which is better: Qwen3 235B-A22B Instruct 2507 or Llama 4 Maverick 17B Instruct?
It is close. Our weighted score puts them within a point (Qwen3 235B-A22B Instruct 2507 58/100, Llama 4 Maverick 17B Instruct 58/100), so choose by what matters most for your work: Qwen3 235B-A22B Instruct 2507 for raw capability and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 235B-A22B Instruct 2507 or Llama 4 Maverick 17B Instruct?
Qwen3 235B-A22B Instruct 2507 is cheaper at $0.15 input / $0.75 output per million tokens (median across 11 API providers). Llama 4 Maverick 17B Instruct costs $0.321 input / $0.91 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.30 per million tokens for Qwen3 235B-A22B Instruct 2507 versus $0.468 for Llama 4 Maverick 17B Instruct (1.6× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B Instruct 2507 scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B Instruct 2507 138.9 (#105 of 148) and Llama 4 Maverick 17B Instruct 132.2 (#122 of 148). Their confidence ranges do not overlap (135.8–140.6 vs 128.0–134.1), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B Instruct 2507 and Llama 4 Maverick 17B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B Instruct 2507 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Llama 4 Maverick 17B Instruct has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3 235B-A22B Instruct 2507. Maximum output per response: Qwen3 235B-A22B Instruct 2507 up to 16,384, Llama 4 Maverick 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B Instruct 2507 accepts text; Llama 4 Maverick 17B Instruct accepts text and images. Llama 4 Maverick 17B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Qwen3 235B-A22B Instruct 2507 is the newest, released Jul 21, 2025. Llama 4 Maverick 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Llama 4 Maverick 17B Instruct Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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