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Model comparison
OpenAI's gpt-oss-20b against Qwen's Qwen3 VL 235B A22B Instruct: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
gpt-oss-20b lists at $0.036 per million tokens blended against $0.632 for Qwen3 VL 235B A22B Instruct - 18x less.
Intelligence index 10 against 10: within a point of each other.
Qwen3 VL 235B A22B Instruct reads 262k tokens at once, gpt-oss-20b 131k.
only gpt-oss-20b offers reasoning; only Qwen3 VL 235B A22B Instruct offers image input.
Qwen3 VL 235B A22B Instruct was released about 2 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | gpt-oss-20b | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Input price / 1M tokens | $0.02 | $0.21 |
| Output price / 1M tokens | $0.09 | $1.90 |
| Blended (3 in : 1 out) | $0.04 | $0.63 |
| Cached input / 1M | $0.009 | $0.10 |
| Context window | 131K | 262K |
| Max output | 33K | 33K |
| Input | text | text, image |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 10 | 10 |
| Coding index | 21 | - |
| Agentic index | 1 | - |
| Released | Aug 5, 2025 | Sep 23, 2025 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | gpt-oss-20b | Qwen3 VL 235B A22B Instruct | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $0.81 | $13.70 | $12.89 less on gpt-oss-20b |
| Document summaries100M in, 5M out - long inputs, short answers. | $2.25 | $30.50 | $28.25 less on gpt-oss-20b |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $2.88 | $50.60 | $47.72 less on gpt-oss-20b |
The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: gpt-oss-20b wins on cost, and a few bad outcomes on either move the next requests to the other.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: gpt-oss-20b, kind: llm, metadata: { model: openai/gpt-oss-20b } }
- { id: qwen3-vl-235b-a22b-instruct, kind: llm, metadata: { model: qwen/qwen3-vl-235b-a22b-instruct } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [gpt-oss-20b]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [qwen3-vl-235b-a22b-instruct]
weight: 0.8