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Model comparison
DeepSeek's DeepSeek V3 0324 against OpenAI's gpt-oss-20b: 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 $0.502 for DeepSeek V3 0324 - 14x less.
Intelligence index 10 against 10: within a point of each other.
Coding index 21 against 21: within a point of each other.
Agentic index 1 against 1: within a point of each other.
DeepSeek V3 0324 reads 164k tokens at once, gpt-oss-20b 131k.
only gpt-oss-20b offers reasoning.
gpt-oss-20b was released about 4 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | DeepSeek V3 0324 | gpt-oss-20b |
|---|---|---|
| Input price / 1M tokens | $0.29 | $0.02 |
| Output price / 1M tokens | $1.14 | $0.09 |
| Blended (3 in : 1 out) | $0.50 | $0.04 |
| Cached input / 1M | $0.11 | $0.009 |
| Context window | 164K | 131K |
| Max output | 115K | 33K |
| Input | text | text |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 10 | 10 |
| Coding index | 21 | 21 |
| Agentic index | 1 | 1 |
| Released | Mar 24, 2025 | Aug 5, 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 | DeepSeek V3 0324 | gpt-oss-20b | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $11.50 | $0.81 | $10.69 less on gpt-oss-20b |
| Document summaries100M in, 5M out - long inputs, short answers. | $34.70 | $2.25 | $32.45 less on gpt-oss-20b |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $40.20 | $2.88 | $37.32 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: deepseek-chat-v3-0324, kind: llm, metadata: { model: deepseek/deepseek-chat-v3-0324 } }
# 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: [deepseek-chat-v3-0324]
weight: 0.8