Ministral 8B Instruct 2410 vs gpt-oss-120b
At a Glance
| Compare |  Ministral 8B Instruct 2410Mistral AI |  gpt-oss-120bOpenAI |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.037Deepinfra ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.17Deepinfra ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 33K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Ministral-8B-Instruct-2410 | gpt-oss-120b |
|---|---|---|
| Developer | Mistral AI | OpenAI |
| Family | Ministral 8b Instruct 2410 | Gpt Oss 120b |
| Model | Ministral-8B-Instruct-2410 | gpt-oss-120b |
| Version | Ministral-8B-Instruct-2410 | gpt-oss-120b |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 33K | 131K |
| Total parameters | 8B | 116.8B |
| Active parameters | Unknown | 5.1B |
| License | other | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Cerebras (Standard), Deepinfra (Standard), Fireworks Ai (Serverless, Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
Ministral 8B Instruct 2410 Capabilities
gpt-oss-120b Capabilities
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Primary Evidence
Sources and Freshness
Questions
Ministral 8B Instruct 2410 vs gpt-oss-120b FAQs
Is Ministral 8B Instruct 2410 or gpt-oss-120b better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Ministral 8B Instruct 2410 and gpt-oss-120b, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Ministral 8B Instruct 2410 or gpt-oss-120b?+
Only gpt-oss-120b has a directly sourced input price: $0.037 per million tokens. Only gpt-oss-120b has a directly sourced output price: $0.17 per million tokens.
Which has a larger context window, Ministral 8B Instruct 2410 or gpt-oss-120b?+
gpt-oss-120b has the larger sourced context window. Ministral 8B Instruct 2410 supports 33K and gpt-oss-120b supports 131K.
Which performs better in benchmarks, Ministral 8B Instruct 2410 or gpt-oss-120b?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Ministral 8B Instruct 2410 or gpt-oss-120b be self-hosted?+
Both models have the same recorded self-hosting status: supported. Ministral 8B Instruct 2410 is open weight; gpt-oss-120b is open weight.
Can Ministral 8B Instruct 2410 and gpt-oss-120b understand images?+
Ministral 8B Instruct 2410 is not documented with image input; gpt-oss-120b is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Ministral 8B Instruct 2410 or gpt-oss-120b?+
Neither has a larger sourced maximum output. Ministral 8B Instruct 2410 is — and gpt-oss-120b is —.
Do Ministral 8B Instruct 2410 and gpt-oss-120b support reasoning and tool use?+
Ministral 8B Instruct 2410: tool calling. gpt-oss-120b: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Ministral 8B Instruct 2410 or gpt-oss-120b?+
Ministral 8B Instruct 2410 has 0 sourced provider routes; gpt-oss-120b has 7, so gpt-oss-120b has broader tracked availability.
Which offers better value, Ministral 8B Instruct 2410 or gpt-oss-120b?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.