Apertus 70B vs Qwen3 VL 235B A22B Thinking vs Qwen3-VL Plus
Qwen3-VL Plus comes out ahead, 56 to 48 and 33 on our weighted score, and it is the cheaper option too.
Swiss AI
Apertus 70B
33/100- ECI—
- Price$0.82 / $2.42
- Context66K
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
- Our pickAlibaba (Qwen)
Qwen3-VL Plus
56/100- ECI—
- Price$0.20 / $1.60
- Context262K
Qwen3-VL Plus is our pick
Qwen3-VL Plus is the better all-round choice, scoring 56/100 against Qwen3 VL 235B A22B Thinking (48) and Apertus 70B (33). It leads on price and context window. Qwen3 VL 235B A22B Thinking wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3-VL PlusQwen3-VL Plus $0.55 · Apertus 70B $1.22 · Qwen3 VL 235B A22B Thinking $1.30 per 1M tokens (3:1 blend)
- Longest contextQwen3-VL PlusQwen3-VL Plus 262,144 · Qwen3 VL 235B A22B Thinking 131,072 · Apertus 70B 65,536 tokens
- Widest inputsQwen3 VL 235B A22B Thinking and Qwen3-VL PlusApertus 70B: Text · Qwen3 VL 235B A22B Thinking: Text, Images · Qwen3-VL Plus: Text, Images
- Self-hostingApertus 70B and Qwen3 VL 235B A22B ThinkingPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Apertus 70B | Qwen3 VL 235B A22B Thinking | Qwen3-VL Plus |
|---|---|---|---|---|
| Price | 50% | 46 | 44 | 62 |
| Inputs & features | 30% | 25 | 70 | 60 |
| Context window | 20% | 12 | 24 | 37 |
| Overall | 100% | 33/100 | 48/100 | 56/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Apertus 70BSwiss AI | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) | Qwen3-VL PlusAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.82 | $0.40 | $0.20 (best) |
| Output | $2.42 | $4.00 | $1.60 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $1.22 | $1.30 | $0.55 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 9 providers | Official Alibaba API |
| Limits | |||
| Context window | 65,536 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens (best) | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Proprietary |
| API model ID | — | — | qwen3-vl-plus |
| API providers | 3 | 9 (best) | 6 |
| Released | Sep 2, 2025 | Sep 23, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Sep 2025 | Mar 31, 2025 | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Apertus 70B$13.04
- Qwen3 VL 235B A22B Thinking$12.00
- Qwen3-VL Plus$5.20
Which should you choose?
Which is better: Apertus 70B, Qwen3 VL 235B A22B Thinking or Qwen3-VL Plus?
Qwen3-VL Plus is the better all-round choice, scoring 56/100 against Qwen3 VL 235B A22B Thinking (48) and Apertus 70B (33). It leads on price and context window. Qwen3 VL 235B A22B Thinking wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Apertus 70B, Qwen3 VL 235B A22B Thinking or Qwen3-VL Plus?
Qwen3-VL Plus is cheaper at $0.20 input / $1.60 output per million tokens (official Alibaba API price). Apertus 70B costs $0.82 input / $2.42 output per million tokens (median across 3 API providers); Qwen3 VL 235B A22B Thinking costs $0.40 input / $4.00 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $0.55 per million tokens for Qwen3-VL Plus versus $1.22 for Apertus 70B (2.2× as much) and $1.30 for Qwen3 VL 235B A22B Thinking (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 70B has not been scored yet, Qwen3 VL 235B A22B Thinking has not been scored yet and Qwen3-VL Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Apertus 70B, Qwen3 VL 235B A22B Thinking and Qwen3-VL Plus yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3-VL Plus has the largest context window at 262,144 tokens, against 131,072 for Qwen3 VL 235B A22B Thinking and 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, Qwen3 VL 235B A22B Thinking up to 32,768, Qwen3-VL Plus up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; Qwen3 VL 235B A22B Thinking accepts text and images; Qwen3-VL Plus accepts text and images. Qwen3 VL 235B A22B Thinking handles the widest range of inputs.
Are any of these open source?
Apertus 70B and Qwen3 VL 235B A22B Thinking publishes its weights (Apache-2.0) and can be self-hosted; Qwen3-VL Plus is proprietary.
Which is newer?
Qwen3 VL 235B A22B Thinking is the newest, released Sep 23, 2025. Qwen3-VL Plus came out Sep 23, 2025; Apertus 70B came out Sep 2, 2025. Knowledge cutoff: Apertus 70B Sep 2025, Qwen3 VL 235B A22B Thinking Mar 31, 2025, Qwen3-VL Plus Apr 2025.
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.
