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Favicon de openai

OpenAI: gpt-oss-20b (batch)

openai/gpt-oss-20b:batch

Poids du modèle
Comparer

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.

Modalités
Prix en entrée / sortie
0,024 $US / 0,112 $USpar 1M
Contexte
131K
Publié
5 août 2025
Date limite des connaissances
juin 2024
Comparer
FournisseursTarificationPerformancesDisponibilitéÉvaluations comparativesApplicationsActivitéFAQExplorer

Fournisseurs

Différentes entreprises hébergent le même modèle. OpenRouter achemine votre requête vers l’une d’elles selon le mode de routage choisi — Balanced (prix + vitesse), Nitro (le plus rapide) ou Floor (le moins cher).

Tarification

Le prix moyen que les clients paient réellement pour ce modèle, à côté des prix affichés par les fournisseurs. Grâce à la mise en cache et aux remises, le prix réellement payé est souvent bien inférieur au prix affiché.

Performances

Le débit est la vitesse à laquelle le modèle écrit (jetons par seconde — plus il est élevé, mieux c’est). La latence est le temps total d’aller-retour (plus il est faible, mieux c’est). Le TTFT est le délai avant le premier jeton — le temps avant de voir quelque chose apparaître (plus il est faible, mieux c’est).

Disponibilité

La disponibilité correspond au pourcentage des 3 derniers jours pendant lequel au moins un fournisseur répondait aux requêtes. La disponibilité du service correspond au pourcentage de temps pendant lequel l’inférence a été assurée avec succès. OpenRouter surveille en continu les fournisseurs et utilise le meilleur fournisseur suivant lorsque l’un d’eux renvoie une erreur.

Évaluations comparatives

Scores aux évaluations standardisées et réponses de ce modèle aux prompts communs des tests de référence. Des pourcentages plus élevés sont meilleurs — et chaque rang indique la position de ce modèle parmi les modèles évalués sur cette source.

Résumé des scores aux tests de performance pour OpenAI: gpt-oss-20b (batch) (Artificial Analysis, Design Arena et Vals AI)
SourceTest de performanceScore
Artificial Analysisgpt-oss-20b (High) Coding Index20,7
Artificial Analysisgpt-oss-20b (High) Agentic Index1,2
Artificial Analysisgpt-oss-20b (High) GPQA Diamond68.8%
Artificial Analysisgpt-oss-20b (High) HLE11.0%
Artificial Analysisgpt-oss-20b (High) IFBench65.1%
Artificial Analysisgpt-oss-20b (High) τ²-Bench Telecom60.2%
Artificial Analysisgpt-oss-20b (High) AA-LCR34.7%
Artificial Analysisgpt-oss-20b (High) τ-Bench Banking7.0%
Artificial Analysisgpt-oss-20b (High) GDPval-AA0.0%
Artificial Analysisgpt-oss-20b (High) CritPt1.4%
Artificial Analysisgpt-oss-20b (High) LiveCodeBench77.7%
Artificial Analysisgpt-oss-20b (High) SciCode38.9%
Artificial Analysisgpt-oss-20b (High) Terminal-Bench Hard10.6%
Artificial Analysisgpt-oss-20b (High) Terminal-Bench 4.00.0%
Artificial Analysisgpt-oss-20b (High) Terminal-Bench 2.113.9%
Artificial Analysisgpt-oss-20b (High) MMLU-Pro74.8%
Artificial Analysisgpt-oss-20b (High) AIME 202589.3%
Artificial Analysisgpt-oss-20b (Low) GPQA Diamond61.1%
Artificial Analysisgpt-oss-20b (Low) HLE5.3%
Artificial Analysisgpt-oss-20b (Low) IFBench57.8%
Artificial Analysisgpt-oss-20b (Low) τ²-Bench Telecom50.3%
Artificial Analysisgpt-oss-20b (Low) AA-LCR31.0%
Artificial Analysisgpt-oss-20b (Low) CritPt0.0%
Artificial Analysisgpt-oss-20b (Low) LiveCodeBench65.2%
Artificial Analysisgpt-oss-20b (Low) Terminal-Bench Hard4.5%
Artificial Analysisgpt-oss-20b (Low) MMLU-Pro71.8%
Artificial Analysisgpt-oss-20b (Low) AIME 202562.3%
Design ArenaGPT OSS 20B Models Arena Data Visualization Elo933
Design ArenaGPT OSS 20B Models Arena Website Elo852
Vals AIGPT OSS 20B AIME86.0%
Vals AIGPT OSS 20B CaseLaw v243.8%
Vals AIGPT OSS 20B CorpFin v253.1%
Vals AIGPT OSS 20B GPQA Diamond68.9%
Vals AIGPT OSS 20B LiveCodeBench80.4%
Vals AIGPT OSS 20B LegalBench70.8%
Vals AIGPT OSS 20B MATH 50094.2%
Vals AIGPT OSS 20B MedQA82.9%
Vals AIGPT OSS 20B MGSM89.0%
Vals AIGPT OSS 20B MMLU Pro71.6%
Vals AIGPT OSS 20B TaxEval v263.7%

Applications

Applications publiques qui envoient le plus de trafic à ce modèle. Un bon indicateur des charges de travail réelles en production — et un aperçu des cas d’usage auxquels ce modèle convient le mieux.

Activité

Volume de jetons et trafic des requêtes vers ce modèle au fil du temps.

API

Code prêt à l’emploi pour appeler ce modèle. L’API d’OpenRouter est compatible avec OpenAI — la plupart des SDK fonctionnent en remplaçant simplement l’URL de base. Seul le slug du modèle ci-dessous change d’un modèle à l’autre.

Actuellement indisponible via le routage dans la région UE. Documentation sur le routage intrarégional

Explorer d’autres modèles

Classements des modèles d’IAClassement
0,024 $US0,112 $US----
--
Évaluations comparatives AutoExacto
GPQA DiamondTAU-BenchDekaLLM68,5 %--SiliconFlow63,0 %--auto-routing66,7 %52,5 %Novita58,1 %--Darkbloom64,7 %51,1 %
+8 fournisseurs supplémentaires

Les données de disponibilité sont encore insuffisantes pour être affichées.

Lorsqu’une erreur survient chez un fournisseur en amont, nous pouvons rétablir le service en routant la requête vers un autre fournisseur opérationnel, si les filtres de votre requête le permettent. Vous pouvez accéder par programmation aux données de disponibilité de chaque fournisseur via l’ API des points de terminaison. Découvrez nos options d’ équilibrage de charge et de routage des fournisseurs.

Pas encore assez de données sur les applications utilisant ce modèle.

Questions fréquentes

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware.

gpt-oss-20b (batch) coûte 0,024 $US/M de jetons d’entrée et 0,112 $US/M de jetons de sortie.

gpt-oss-20b (batch) dispose d’une fenêtre de contexte de 131 072 jetons. Il prend en charge jusqu’à 131 072 jetons de complétion.

Oui. gpt-oss-20b (batch) accepte tools et tool_choice pour l’appel de fonctions. Il prend également en charge les sorties structurées via un schéma JSON dans response_format.

GPT-6.1 Terra Pro, GPT-6.1 Terra, GPT-6 Luna Decisions et 58 de plus sont d’autres modèles de type texte de OpenAI.

gpt-oss-20b (batch) est sorti le 5 août 2025. Ses connaissances s’arrêtent au 30 juin 2024.

Autres modèles de OpenAI

GPT-6.1 Terra Pro

GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Terra and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Terra instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

TexteContexte de 1,1M1 $US / 5 $US
GPT-6.1 Terra Pro (batch)

GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Terra and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Terra instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

TexteContexte de 1,1M0,50 $US / 2,50 $US
GPT-6.1 Terra

GPT-6.1 Terra is the mid-tier model in OpenAI's GPT-6.1 series, priced below GPT-6.1 Sol. It is suited for everyday agentic, coding, and writing work, is particularly strong at understanding screenshots and on-screen content, and is efficient enough for high-volume workloads.

TexteContexte de 1,1M1 $US / 5 $US
GPT-6.1 Terra (batch)

GPT-6.1 Terra is the mid-tier model in OpenAI's GPT-6.1 series, priced below GPT-6.1 Sol. It is suited for everyday agentic, coding, and writing work, is particularly strong at understanding screenshots and on-screen content, and is efficient enough for high-volume workloads.

TexteContexte de 1,1M0,50 $US / 2,50 $US
GPT-6 Luna Decisions

GPT-6 Luna Decisions is GPT-6 Luna served through OpenAI's Decisions API. Instead of generating text, it reads the content passed as state (text, JSON, or images) and returns typed, probabilistic answers to named questions in a single request: the probability of yes for a yes/no question (noul), a probability for every option plus the most likely one (choice), or a probability for every level of an ordered rubric plus the expected score (score).

It is built for classification, routing, moderation, and rubric grading where application code thresholds the returned numbers rather than parsing a chat reply. A request can carry up to 200 questions about the same content.

Décisions0,10 $US / 0 $US
GPT-6.1 Sol Pro

GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

TexteContexte de 1,1M2 $US / 10 $US
GPT-6.1 Sol Pro (batch)

GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

TexteContexte de 1,1M1 $US / 5 $US
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GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

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GPT-6.1 Sol (batch)

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

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GPT-6 Luna Pro

GPT-6 Luna Pro is the same underlying model as GPT-6 Luna, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Luna and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Luna instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

TexteContexte de 1,1M0,10 $US / 0,50 $US
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Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Luna and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Luna instead.

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