OpenAI: GPT-6.1 Sol Pro (batch) 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 by openai Sep 29, 2026 1.05M context $1 /M input tokens $5 /M output tokens 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 OpenAI: GPT-6.1 Sol Pro GPT-6.1 Sol Pro 3.11B tokens 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 by openai Sep 29, 2026 1.05M context $2 /M input tokens $10 /M output tokens 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 OpenAI: GPT-6.1 Sol (batch) 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. by openai Sep 29, 2026 1.05M context $1 /M input tokens $5 /M output tokens 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. OpenAI: GPT-6.1 Sol GPT-6.1 Sol 71B tokens SEO (#40) 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. by openai Sep 29, 2026 1.05M context $2 /M input tokens $10 /M output tokens 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. Anthropic: Claude Sonnet 5.5 (batch) Claude Sonnet 5.5 (batch) 101M tokens Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade. It is especially strong at building features, fixing bugs, and producing polished documents, slides, and spreadsheets, and it writes and communicates more clearly than its predecessor. Thinking is always on, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings keep it responsive for everyday agentic loops. by anthropic Sep 28, 2026 1M context $1 /M input tokens $5 /M output tokens Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade. It is especially strong at building features, fixing bugs, and producing polished documents, slides, and spreadsheets, and it writes and communicates more clearly than its predecessor. Thinking is always on, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings keep it responsive for everyday agentic loops. Anthropic: Claude Sonnet 5.5 Claude Sonnet 5.5 183B tokens Finance (#45) Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade. It is especially strong at building features, fixing bugs, and producing polished documents, slides, and spreadsheets, and it writes and communicates more clearly than its predecessor. Thinking is always on, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings keep it responsive for everyday agentic loops. by anthropic Sep 28, 2026 1M context $2 /M input tokens $10 /M output tokens Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, succeeding Claude Sonnet 5 as a direct upgrade. It is especially strong at building features, fixing bugs, and producing polished documents, slides, and spreadsheets, and it writes and communicates more clearly than its predecessor. Thinking is always on, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings keep it responsive for everyday agentic loops. Upstage: Solar Decide Solar Decide 50% off 1.94B tokens Solar Decide is Upstage's structured decision model, served as a System One endpoint on Solar Mini 4. Send a state along with typed questions, and it returns a choice, a score, or a yes/no answer, each with a calibrated probability taken directly from the model rather than written out as text. Because it generates no prose, each decision takes a single forward pass and output tokens are free. With a 512K context window, an entire document can serve as the state. Solar Decide uses the same /v1/systemone schema as Jev, bringing Solar Mini 4's strong Korean understanding to routing, classification, and policy checks. by upstage Sep 28, 2026 524K context $0.05 /M input tokens $0 /M output tokens Solar Decide is Upstage's structured decision model, served as a System One endpoint on Solar Mini 4. Send a state along with typed questions, and it returns a choice, a score, or a yes/no answer, each with a calibrated probability taken directly from the model rather than written out as text. Because it generates no prose, each decision takes a single forward pass and output tokens are free. With a 512K context window, an entire document can serve as the state. Solar Decide uses the same /v1/systemone schema as Jev, bringing Solar Mini 4's strong Korean understanding to routing, classification, and policy checks. Respan: Span-01 Span-01 4.89B tokens Span-01 is a behavior scoring model from Respan. It reads a conversation span and returns, for each plain-language behavior you define, the probability that the behavior is present. It is suited for evaluation, guardrails, and monitoring of LLM and agent outputs at scale. It is the higher-accuracy tier of the family. Span-01 Lite is the free, lighter tier. by respan Sep 26, 2026 $0.02 /M input tokens $0 /M output tokens Span-01 is a behavior scoring model from Respan. It reads a conversation span and returns, for each plain-language behavior you define, the probability that the behavior is present. It is suited for evaluation, guardrails, and monitoring of LLM and agent outputs at scale. It is the higher-accuracy tier of the family. Span-01 Lite is the free, lighter tier. Respan: Span-01 Lite Span-01 Lite 10.8B tokens Span-01 Lite is the free, lighter tier of Span-01, a behavior scoring model from Respan. It returns, for each plain-language behavior you define, the probability that the behavior is present in a conversation span, and is suited for high-volume evaluation and monitoring where cost matters more than peak accuracy. by respan Sep 26, 2026 $0 /M input tokens $0 /M output tokens Span-01 Lite is the free, lighter tier of Span-01, a behavior scoring model from Respan. It returns, for each plain-language behavior you define, the probability that the behavior is present in a conversation span, and is suited for high-volume evaluation and monitoring where cost matters more than peak accuracy. Respan: Span-01 Lite (free) Span-01 Lite (free) 161M tokens Span-01 Lite is the free, lighter tier of Span-01, a behavior scoring model from Respan. It returns, for each plain-language behavior you define, the probability that the behavior is present in a conversation span, and is suited for high-volume evaluation and monitoring where cost matters more than peak accuracy. by respan Sep 26, 2026 $0 /M input tokens $0 /M output tokens Span-01 Lite is the free, lighter tier of Span-01, a behavior scoring model from Respan. It returns, for each plain-language behavior you define, the probability that the behavior is present in a conversation span, and is suited for high-volume evaluation and monitoring where cost matters more than peak accuracy.

ByteDance Seed: Seed Audio 1.0 Seed Audio 1.0 915K tokens Seed Audio 1.0 is ByteDance Seed's non-streaming audio generation model. It produces speech and other audio from a natural-language text prompt that can describe the desired voice, tone, and sound effects, optionally guided by a Seed speaker ID or a reference audio clip for voice cloning. Output is limited to 120 seconds per request and is billed per second of generated audio. Suited to audiobooks, voiceovers, games, and similar workloads. by bytedance-seed Sep 25, 2026 $0.15/minute Seed Audio 1.0 is ByteDance Seed's non-streaming audio generation model. It produces speech and other audio from a natural-language text prompt that can describe the desired voice, tone, and sound effects, optionally guided by a Seed speaker ID or a reference audio clip for voice cloning. Output is limited to 120 seconds per request and is billed per second of generated audio. Suited to audiobooks, voiceovers, games, and similar workloads. TypeSafe: Jev Router Jev Router Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. It runs on Jev , TypeSafe's first System One model, and adapts as your conversation evolves. One endpoint gives you the whole model ecosystem, leveraging the ecosystem's token and spend share. by typesafe Sep 25, 2026 1M context Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. It runs on Jev , TypeSafe's first System One model, and adapts as your conversation evolves. One endpoint gives you the whole model ecosystem, leveraging the ecosystem's token and spend share. Jared Palmer: Kev 4B Kev 4B 727M tokens Kev 4B is a small open-weight decision model from Jared Palmer, built as a LoRA adapter and pointer head on Qwen3.5-4B-Base and served over the same /v1/systemone contract as TypeSafe's Jev. Send a state and typed questions (yes/no, multiple choice, or score) and it returns a calibrated probability per question in one forward pass, with no generated text. It is the recommended checkpoint in the Kev family (0.8B, 4B, 9B) and is suited for routing, classification, and policy checks that want a compact, Apache-2.0 alternative to Jev. Code, model cards, and eval suites: https://github.com/jaredpalmer/kev by jaredpalmer Sep 25, 2026 8K context $0.042 /M input tokens $0 /M output tokens Kev 4B is a small open-weight decision model from Jared Palmer, built as a LoRA adapter and pointer head on Qwen3.5-4B-Base and served over the same /v1/systemone contract as TypeSafe's Jev. Send a state and typed questions (yes/no, multiple choice, or score) and it returns a calibrated probability per question in one forward pass, with no generated text. It is the recommended checkpoint in the Kev family (0.8B, 4B, 9B) and is suited for routing, classification, and policy checks that want a compact, Apache-2.0 alternative to Jev. Code, model cards, and eval suites: https://github.com/jaredpalmer/kev Perceptron: Perceptron Mk1.5 Perceptron Mk1.5 855M tokens Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks, and clips. It supports graded reasoning through the standard reasoning controls, function tool calling, and structured outputs via JSON Schema. Structured annotations are emitted inline with text only when requested via the annotation_format parameter ( "point" , "box" , or "polygon" for spatial localization on images, "clip" for temporal segments in video). Video soundtracks are analyzed only when explicitly enabled per request. by perceptron Sep 25, 2026 37K context $0.15 /M input tokens $1.50 /M output tokens Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks, and clips. It supports graded reasoning through the standard reasoning controls, function tool calling, and structured outputs via JSON Schema. Structured annotations are emitted inline with text only when requested via the annotation_format parameter ( "point" , "box" , or "polygon" for spatial localization on images, "clip" for temporal segments in video). Video soundtracks are analyzed only when explicitly enabled per request. Google: Gemini 3.5 Transcribe Gemini 3.5 Transcribe 59.1M characters Gemini 3.5 Transcribe is a speech-to-text model from Google. It is suited for synchronous transcription that needs word-level timestamps or speaker diarization, with support for up to eight speakers. Audio can be up to one hour, or 30 minutes when timestamps or diarization are enabled. by google Sep 25, 2026 98K context $2 /M input tokens $12 /M output tokens Gemini 3.5 Transcribe is a speech-to-text model from Google. It is suited for synchronous transcription that needs word-level timestamps or speaker diarization, with support for up to eight speakers. Audio can be up to one hour, or 30 minutes when timestamps or diarization are enabled. Fish Audio: Transcribe 1 Pro Transcribe 1 Pro 15.9M characters Transcribe 1 Pro is a speech-to-text model from Fish Audio tuned for interviews, meetings, and podcasts. It labels speakers with inline speaker markers, preserves emotion and vocal-event cues such as [laughter] , detects language automatically, and can return timestamped word-level segments. by fish-audio Sep 24, 2026 $0.0001/second Transcribe 1 Pro is a speech-to-text model from Fish Audio tuned for interviews, meetings, and podcasts. It labels speakers with inline speaker markers, preserves emotion and vocal-event cues such as [laughter] , detects language automatically, and can return timestamped word-level segments. Fireworks: Ember-1 Ember-1 6.24B tokens Ember-1 is a specialized reasoning model from Fireworks Research, built on Kimi K3 . It is designed to make every token go further: it produces shorter reasoning traces, using roughly 40% fewer tokens than the base model while maintaining comparable quality across Fireworks' evaluations. It is suited for coding, knowledge work, and agentic workflows where reasoning cost and latency matter. by fireworks Sep 24, 2026 1.05M context $3 /M input tokens $15 /M output tokens Ember-1 is a specialized reasoning model from Fireworks Research, built on Kimi K3 . It is designed to make every token go further: it produces shorter reasoning traces, using roughly 40% fewer tokens than the base model while maintaining comparable quality across Fireworks' evaluations. It is suited for coding, knowledge work, and agentic workflows where reasoning cost and latency matter. inclusionAI: Ming Image 0.1 Design Layer Ming Image 0.1 Design Layer 1.08B tokens Ming Image 0.1 Design Layer is an image-to-image model from inclusionAI that decomposes a flattened design image into separate RGBA layers, such as a background layer and foreground elements, and returns one image per layer. It requires exactly one reference image and a prompt describing the layer plan. Output format can be requested as PNG or WebP. Output dimensions follow the input image, so explicit sizes and aspect ratios are rejected instead of silently reshaped. by inclusionai Sep 23, 2026 $0 /M input tokens $0 /M output tokens Ming Image 0.1 Design Layer is an image-to-image model from inclusionAI that decomposes a flattened design image into separate RGBA layers, such as a background layer and foreground elements, and returns one image per layer. It requires exactly one reference image and a prompt describing the layer plan. Output format can be requested as PNG or WebP. Output dimensions follow the input image, so explicit sizes and aspect ratios are rejected instead of silently reshaped. Google: Gemini 3.8 Flash Lite TTS Gemini 3.8 Flash Lite TTS 107M tokens Gemini 3.8 Flash Lite TTS is a text-to-speech model from Google and the fast, high-throughput member of the 3.8 TTS family alongside Gemini 3.8 Flash TTS . It is suited for conversational voice-agent pipelines, high-volume read-aloud, and voice replication workloads where latency and cost matter more than maximum expressiveness. It accepts the same speech configuration as Gemini 3.1 Flash TTS Preview , including the 30 prebuilt studio voices, and supports Google's Voice Design and Voice Replication through voice_... identifiers. by google Sep 23, 2026 8K context $0.50 /M input tokens $6 /M output tokens Gemini 3.8 Flash Lite TTS is a text-to-speech model from Google and the fast, high-throughput member of the 3.8 TTS family alongside Gemini 3.8 Flash TTS . It is suited for conversational voice-agent pipelines, high-volume read-aloud, and voice replication workloads where latency and cost matter more than maximum expressiveness. It accepts the same speech configuration as Gemini 3.1 Flash TTS Preview , including the 30 prebuilt studio voices, and supports Google's Voice Design and Voice Replication through voice_... identifiers. Google: Gemini 3.8 Flash TTS Gemini 3.8 Flash TTS 370M tokens Gemini 3.8 Flash TTS is a text-to-speech model from Google and the successor to Gemini 3.1 Flash TTS Preview . It is the creative tier of the 3.8 TTS family, suited for narration, character work, and multi-speaker dialogue where voice fidelity, acting nuance, and regional dialect coverage matter more than latency. It accepts the same speech configuration as its predecessor, including the 30 prebuilt studio voices, and supports Google's Voice Design (custom voices from a text description) and Voice Replication (voices cloned from a short sample) through voice_... identifiers. by google Sep 23, 2026 8K context $0.50 /M input tokens $9 /M output tokens Gemini 3.8 Flash TTS is a text-to-speech model from Google and the successor to Gemini 3.1 Flash TTS Preview . It is the creative tier of the 3.8 TTS family, suited for narration, character work, and multi-speaker dialogue where voice fidelity, acting nuance, and regional dialect coverage matter more than latency. It accepts the same speech configuration as its predecessor, including the 30 prebuilt studio voices, and supports Google's Voice Design (custom voices from a text description) and Voice Replication (voices cloned from a short sample) through voice_... identifiers.