Documentation Index
Fetch the complete documentation index at: https://docs.together.ai/llms.txt
Use this file to discover all available pages before exploring further.
GPU Clusters: External OIDC authentication and RBAC
GPU clusters now support external OpenID Connect (OIDC) authentication, allowing each team member to access the cluster’s Kubernetes API using their organization’s identity provider — Google, Okta, Auth0, Microsoft Entra ID, and others.With OIDC enabled, access is managed through standard Kubernetes RBAC: admins bind permissions to individual user identities, and each user authenticates via their browser using SSO. This replaces shared kubeconfig credentials with per-user tokens, per-user audit trails, and clean revocation. Currently this feature is only supported for Kubernetes clusters.OIDC must be configured at cluster creation time. See Set up OIDC authentication for the full setup guide.New serverless models
The following model has been added to serverless:
Qwen/Qwen3.7-Max. Pricing: $2.50 input / $7.50 output (per 1M tokens).
See Serverless models.Model deprecations
Deprecated serverless endpoint for:
Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8. Recommended replacement: MiniMaxAI/MiniMax-M2.7. Available as an on-demand dedicated endpoint.
See Deprecations for migration options.Model deprecations
The following model has been deprecated and is no longer available on serverless:See Deprecations for migration options.New serverless models
The following model has been added to serverless:
pearl-ai/gemma-4-31b-it: 32,000 context length, INT8 quantization. Pricing: $0.28 input / $0.86 output (per 1M tokens).
See Serverless models.Model deprecations
The following models have been deprecated and are no longer available on serverless:
deepseek-ai/DeepSeek-R1.deepseek-ai/DeepSeek-V3.1.Qwen/Qwen3-Coder-Next-FP8.
Upcoming pricing update
The following model will have updated pricing, effective May 21, 2026:
google/gemma-4-31b-it: $0.20 → $0.39 (input), $0.50 → $0.97 (output) per 1M tokens.
All usage from that date forward will be billed at the new rate.External collaborators for projects
You can now invite users from outside your organization to collaborate on a project. Enable Allow external collaborators on the project’s settings page, then add them like any other collaborator. The feature is currently in beta. See roles & permissions for more details.New serverless models
The following models have been added to serverless:
alibaba/happyhorse-1.0-t2v: $0.24/sec at 1080p.ByteDance/Seedance-2.0: $0.16/sec at 720p.
See Serverless models.Together CLI v2.10
The Together CLI has been updated with tg as the canonical command name and a refreshed command tree. Subcommands are now clearer and more consistent across fine-tuning, endpoints, evals, files, clusters, and jig.See CLI reference for details.Speech-to-text and translation: new audio formats
The /v1/audio/transcriptions and /v1/audio/translations endpoints now accept .ogg, .opus, and .aac files in addition to .wav, .mp3, .m4a, .webm, and .flac.Speech-to-text: task field is now optional in verbose JSON responses
The task field has been removed from the required fields of AudioTranscriptionVerboseJsonResponse and AudioTranslationVerboseJsonResponse. Clients that previously asserted on its presence should treat it as optional.Slurm-on-Kubernetes v1.0 for all new Slurm clusters
All newly provisioned Slurm GPU clusters now run on a new Slurm-on-Kubernetes stack with significant reliability improvements. Existing clusters can be migrated in place.What’s new:
- Self-healing worker daemons: The Slurm worker daemon is now supervised and auto-restarts on crash, so transient failures recover without operator intervention or impact on healthy nodes.
- Durable job accounting: Job history (
sacct) is now persisted on durable, PVC-backed storage. Restarts and pod reschedules no longer wipe accounting data.
- Correct process tracking and cleanup: Job processes (including daemonized children) are tracked at the kernel cgroup level and reliably cleaned up at job completion. No more orphaned processes holding GPU memory or
/dev/shm.
- Zombie reaping: A dedicated init process reaps orphaned children, preventing PID-table exhaustion from blocking new jobs.
- GPU state correctness: The Slurm GPU view is rebuilt fresh on every node start, eliminating “GPU not found” failures after pod reschedules.
- Per-cluster GPU utilization metrics: DCGM metrics are now exposed in your cluster’s Grafana dashboards for fine-grained utilization visibility.
See Slurm configuration for more details.Model deprecations
The following models have been deprecated and are no longer available on serverless:Text-to-speech: pronunciation_dict parameter
A new pronunciation_dict parameter is available for TTS requests. Pass a list of "<source>/<replacement>" rules (e.g., ["omg/oh my god"]) to override how the model pronounces specific tokens.Together Deployments: custom metric autoscaling
Deployments can now autoscale on any Prometheus metric exposed by your worker’s /metrics endpoint. Set metric = "CustomMetric" and provide a custom_metric_name (e.g., vllm:num_requests_running) along with a target to scale on application-specific signals.Fine-tuning: new supported models
The following models are now available for fine-tuning:
Qwen/Qwen3.6-35B-A3B.google/gemma-4-31B-it.google/gemma-4-26B-A4B-it.
DeepSeek-V4-Pro on serverless
deepseek-ai/DeepSeek-V4-Pro has been added to serverless.
- Context length: 512,000.
- Pricing: $2.10 input / $4.40 output / $0.20 cached input (per 1M tokens).
- Quantization: FP4.
- Function calling and structured outputs supported.
New serverless models
The following models have been added:
deepcogito/cogito-v2-1-671b.google/veo-3.1-test-debug.vidu/vidu-q3.vidu/vidu-q3-turbo.Wan-AI/wan2.7-i2v.Wan-AI/wan2.7-r2v.
Pricing update: no-packing fine-tuning jobs
We rolled out a pricing update for no-packing fine-tuning jobs. When the no-packing option is chosen, the number of training dataset tokens is now calculated as len(dataset) * max_seq_length to account for the compute used by packing-free jobs.
max_seq_length is configurable in both the SDK and UI.- Price prediction reflects these changes, so if no-packing is chosen you can control the cost of the job by adjusting the sequence length.
Dynamic rate limits and prepaid billing
- Build Tiers 1–5, Scale, and Enterprise tier labels have been retired. Dynamic rate limits are now live for all users.
- Billing has moved to a fully prepaid model.
- Model-specific tier gates have been removed. The platform-wide $5 credit purchase is the only gate.
New serverless models
The following models have been added:Pricing update
The following model has updated pricing, effective April 15, 2026:
google/gemma-3n-E4B-it: $0.02 → $0.06 (input), $0.04 → $0.12 (output) per 1M tokens.
Model deprecations
The following models have been deprecated and are no longer available:
Qwen/Qwen3-VL-8B-Instruct.Qwen/Qwen3-235B-A22B-Thinking-2507.mistralai/Mixtral-8x7B-Instruct-v0.1.
New serverless models
The following models have been added:New serverless models
The following models have been added:
google/gemma-4-31B-it.zai-org/GLM-5.1.
Model deprecations
The following models have been deprecated and are no longer available:
zai-org/GLM-4.5-Air-FP8.zai-org/GLM-4.7.Qwen/Qwen3-Next-80B-A3B-Instruct.
Model deprecation
The following model has been deprecated and is no longer available:
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8.
Cached input token pricing is now available:
MiniMaxAI/MiniMax-M2.5: $0.06 per 1M cached input tokens (80% off standard input price).
New serverless models
The following models have been added:Model deprecations
The following models have been deprecated and are no longer available:
mixedbread-ai/Mxbai-Rerank-Large-V2.moonshotai/Kimi-K2-Thinking.meta-llama/Llama-3.2-3B-Instruct-Turbo.moonshotai/Kimi-K2-Instruct-0905.
Model deprecations
The following models have been deprecated and are no longer available:
black-forest-labs/FLUX.1-dev.black-forest-labs/FLUX.1-dev-lora.black-forest-labs/FLUX.1-kontext-dev.Qwen/Qwen3-VL-32B-Instruct.mistralai/Ministral-3-14B-Instruct-2512.Qwen/Qwen3-Next-80B-A3B-Thinking.Alibaba-NLP/gte-modernbert-base.BAAI/bge-base-en-v1.5.meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo.meta-llama/Llama-Guard-3-11B-Vision-Turbo.meta-llama/LlamaGuard-2-8b.marin-community/marin-8b-instruct.nvidia/NVIDIA-Nemotron-Nano-9B-v2.
New serverless models
The following models have been added:New serverless models
The following models have been added:New serverless models
The following models have been added:Dedicated Container Inference launch
Together AI has officially launched Dedicated Container Inference (DCI), formerly known as BYOC. DCI lets you containerize, deploy, and scale custom models on Together AI.Model deprecations
The following models have been deprecated and are no longer available:
togethercomputer/m2-bert-80M-32k-retrieval.Salesforce/Llama-Rank-V1.togethercomputer/Refuel-Llm-V2.togethercomputer/Refuel-Llm-V2-Small.Qwen/Qwen3-235B-A22B-fp8-tput.qwen-qwen2-5-14b-instruct-lora.meta-llama/Llama-4-Scout-17B-16E-Instruct.Qwen/Qwen2.5-72B-Instruct-Turbo.meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo.BAAI/bge-large-en-v1.5.
Python SDK v2.0 general availability
Together AI is releasing the Python SDK v2.0, a new, type-safe, OpenAPI-driven client designed to be faster, easier to maintain, and ready for everything we’re building next.
- Install:
pip install together or uv add together.
- Migration guide: A detailed Python SDK Migration Guide covers API-by-API changes, type updates, and troubleshooting tips.
- Code and docs: Access the Together Python v2 repo and reference docs with code examples.
- Main goal: Replace the legacy v1 Python SDK with a modern, strongly-typed, OpenAPI-generated client that matches the API surface more closely and stays in lock-step with new features.
- Net new: All new features will be built in version 2 moving forward. This first version already includes beta APIs for our Instant Clusters.
New serverless models
The following models have been added:
Qwen/Qwen3-Coder-Next-FP8.
Model deprecations
The following models have been deprecated and are no longer available:
deepseek-ai/DeepSeek-R1-0528-tput.
Model redirects
The following models are now being automatically redirected to their upgraded versions. See our Model Lifecycle Policy for details.| Original model | Redirects to |
|---|
mistralai/Mistral-7B-Instruct-v0.3 | mistralai/Ministral-3-14B-Instruct-2512 |
zai-org/GLM-4.6 | zai-org/GLM-4.7 |
New serverless models
The following models have been added:Model redirect
The following model is now being automatically redirected to its upgraded version. See our Model Lifecycle Policy for details.| Original model | Redirects to |
|---|
DeepSeek-V3-0324 | DeepSeek-V3.1 |
Prompt caching now enabled by default for dedicated endpoints
Prompt caching is now automatically enabled for all newly created dedicated endpoints. This change improves performance and reduces costs by default.What’s changing:
- The
disable_prompt_cache field (API), --no-prompt-cache flag (CLI), and related SDK parameters are now deprecated.
- Prompt caching will always be enabled. The field is accepted but ignored after deprecation.
Timeline:
- Now: Field is deprecated; setting it has no effect (prompt caching is always on).
- February 2026: Field will be removed.
Action required:
--no-prompt-cache in CLI commands has no effect. You can remove it.disable_prompt_cache from API requests has no effect. You can remove it.- SDK calls that set this parameter have no effect. You can remove it.
No changes are required for existing endpoints. This only affects endpoint creation.New serverless models
The following models have been added:Model deprecations
The following models have been deprecated and are no longer available:
Qwen/Qwen2.5-VL-72B-Instruct.
Model deprecations
The following models have been deprecated and are no longer available:
deepseek-ai/DeepSeek-R1-Distill-Llama-70B.meta-llama/Meta-Llama-3-70B-Instruct-Turbo.black-forest-labs/FLUX.1-schnell-free.meta-llama/Meta-Llama-Guard-3-8B.
Model redirects now active
The following models are now being automatically redirected to their upgraded versions. See our Model Lifecycle Policy for details.| Original model | Redirects to |
|---|
Kimi-K2 | Kimi-K2-0905 |
DeepSeek-V3 | DeepSeek-V3-0324 |
DeepSeek-R1 | DeepSeek-R1-0528 |
Python SDK v2.0 release candidate
Together AI is releasing the Python SDK v2.0 Release Candidate, a new, OpenAPI-generated, strongly-typed client that replaces the legacy v1.0 package and brings the SDK into lock-step with the latest platform features.
- Install:
pip install together==2.0.0a9.
- RC period: The v2.0 RC window starts today and will run for approximately one month. During this time we’ll iterate quickly based on developer feedback and may make a few small, well-documented breaking changes before GA.
- Type-safe, modern client: Stronger typing across parameters and responses, keyword-only arguments, explicit
NOT_GIVEN handling for optional fields, and rich together.types.* definitions for chat messages, eval parameters, and more.
- Redesigned error model: Replaces
TogetherException with a new TogetherError hierarchy, including APIStatusError and specific HTTP status code errors such as BadRequestError (400), AuthenticationError (401), RateLimitError (429), and InternalServerError (5xx), plus transport (APIConnectionError, APITimeoutError) and validation (APIResponseValidationError) errors.
- New Jobs API: Adds first-class support for the Jobs API (
client.jobs.*) so you can create, list, and inspect asynchronous jobs directly from the SDK without custom HTTP wrappers.
- New Hardware API: Adds the Hardware API (
client.hardware.*) to discover available hardware, filter by model compatibility, and compute effective hourly pricing from cents_per_minute.
- Raw response and streaming helpers: New
.with_raw_response and .with_streaming_response helpers make it easier to debug, inspect headers and status codes, and stream completions via context managers with automatic cleanup.
- Code Interpreter sessions: Adds session management for the Code Interpreter (
client.code_interpreter.sessions.*), enabling multi-step, stateful code-execution workflows that were not possible in the legacy SDK.
- High compatibility for core APIs: Most core usage patterns, including
chat.completions, completions, embeddings, images.generate, audio transcription/translation/speech, rerank, fine_tuning.create/list/retrieve/cancel, and models.list, are designed to be drop-in compatible between v1 and v2.
- Targeted breaking changes: Some APIs (Files, Batches, Endpoints, Evals, Code Interpreter, select fine-tuning helpers) have updated method names, parameters, or response shapes; these are fully documented in the Python SDK Migration Guide and Breaking Changes notes.
- Migration resources: A dedicated Python SDK Migration Guide is available with API-by-API before/after examples, a feature parity matrix, and troubleshooting tips to help teams smoothly transition from v1 to v2 during the RC period.
New serverless models
The following models have been added:
mistralai/Ministral-3-14B-Instruct-2512.
New serverless models
The following models have been added:
zai-org/GLM-4.6.moonshotai/Kimi-K2-Thinking.
Real-time text-to-speech and speech-to-text
Together AI expands audio capabilities with real-time streaming for both TTS and STT, new models, and speaker diarization.
- Real-time text-to-speech: WebSocket API for lowest-latency interactive applications.
- New TTS models: Orpheus 3B (
canopylabs/orpheus-3b-0.1-ft) and Kokoro 82M (hexgrad/Kokoro-82M), supporting REST, streaming, and WebSocket endpoints.
- Real-time speech-to-text: WebSocket streaming transcription with Whisper for live audio applications.
- Voxtral model: New Mistral AI speech recognition model (
mistralai/Voxtral-Mini-3B-2507) for audio transcriptions.
- Speaker diarization: Identify and label different speakers in audio transcriptions with a free
diarize flag.
- TTS WebSocket endpoint:
/v1/audio/speech/websocket.
- STT WebSocket endpoint:
/v1/realtime.
See the Text-to-speech guide and Speech-to-text guide.Image model deprecations
The following image models have been deprecated and are no longer available:
black-forest-labs/FLUX.1-pro (calls to FLUX.1-pro will now redirect to FLUX.1.1-pro).black-forest-labs/FLUX.1-Canny-pro.
Video generation API and 40+ new image and video models
Together AI expands into multimedia generation with comprehensive video and image capabilities. Read more.
- New video generation API: Create high-quality videos with models like OpenAI Sora 2, Google Veo 3.0, and Minimax Hailuo.
- 40+ image and video models: Including Google Imagen 4.0 Ultra, Gemini Flash Image 2.5 (Nano Banana), ByteDance SeeDream, and specialized editing tools.
- Unified platform: Combine text, image, and video generation through the same APIs, authentication, and billing.
- Production-ready: Serverless endpoints with transparent per-model pricing and enterprise-grade infrastructure.
- Video endpoints:
/videos/create and /videos/retrieve.
- Image endpoint:
/images/generations.
Improved Batch Inference API
- Streamlined UI: Create and track batch jobs in an intuitive interface. No complex API calls required.
- Universal model access: The Batch Inference API now supports all serverless models and private deployments, so you can run batch workloads on exactly the models you need.
- Massive scale jump: Rate limits are up from 10M to 30B enqueued tokens per model per user, a 3,000x increase. Need more? We’ll work with you to customize.
- Lower cost: For most serverless models, the Batch Inference API runs at 50% the cost of our real-time API, making it the most economical way to process high-throughput workloads.
Qwen3-Next-80B models
New Qwen3-Next-80B models are now available for both thinking and instruction tasks.
- Model ID:
Qwen/Qwen3-Next-80B-A3B-Thinking.
- Model ID:
Qwen/Qwen3-Next-80B-A3B-Instruct.
Fine-tuning: new large models supported
Enhanced fine-tuning capabilities with expanded model support. Read more.
openai/gpt-oss-120b.deepseek-ai/DeepSeek-V3.1.deepseek-ai/DeepSeek-V3.1-Base.deepseek-ai/DeepSeek-R1-0528.deepseek-ai/DeepSeek-R1.deepseek-ai/DeepSeek-V3-0324.deepseek-ai/DeepSeek-V3.deepseek-ai/DeepSeek-V3-Base.Qwen/Qwen3-Coder-480B-A35B-Instruct.Qwen/Qwen3-235B-A22B (context length 32,768 for SFT and 16,384 for DPO).Qwen/Qwen3-235B-A22B-Instruct-2507 (context length 32,768 for SFT and 16,384 for DPO).meta-llama/Llama-4-Maverick-17B-128E.meta-llama/Llama-4-Maverick-17B-128E-Instruct.meta-llama/Llama-4-Scout-17B-16E.meta-llama/Llama-4-Scout-17B-16E-Instruct.
Fine-tuning: increased maximum context lengths
DeepSeek models
- DeepSeek-R1-Distill-Llama-70B: SFT 8,192 → 24,576; DPO 8,192 → 8,192.
- DeepSeek-R1-Distill-Qwen-14B: SFT 8,192 → 65,536; DPO 8,192 → 12,288.
- DeepSeek-R1-Distill-Qwen-1.5B: SFT 8,192 → 131,072; DPO 8,192 → 16,384.
Google Gemma models
- gemma-3-1b-it: SFT 16,384 → 32,768; DPO 16,384 → 12,288.
- gemma-3-1b-pt: SFT 16,384 → 32,768; DPO 16,384 → 12,288.
- gemma-3-4b-it: SFT 16,384 → 131,072; DPO 16,384 → 12,288.
- gemma-3-4b-pt: SFT 16,384 → 131,072; DPO 16,384 → 12,288.
- gemma-3-12b-pt: SFT 16,384 → 65,536; DPO 16,384 → 8,192.
- gemma-3-27b-it: SFT 12,288 → 49,152; DPO 12,288 → 8,192.
- gemma-3-27b-pt: SFT 12,288 → 49,152; DPO 12,288 → 8,192.
Qwen models
- Qwen3-0.6B / Qwen3-0.6B-Base: SFT 8,192 → 32,768; DPO 8,192 → 24,576.
- Qwen3-1.7B / Qwen3-1.7B-Base: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen3-4B / Qwen3-4B-Base: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen3-8B / Qwen3-8B-Base: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen3-14B / Qwen3-14B-Base: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen3-32B: SFT 8,192 → 24,576; DPO 8,192 → 4,096.
- Qwen2.5-72B-Instruct: SFT 8,192 → 24,576; DPO 8,192 → 8,192.
- Qwen2.5-32B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 12,288.
- Qwen2.5-32B: SFT 8,192 → 49,152; DPO 8,192 → 12,288.
- Qwen2.5-14B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2.5-14B: SFT 8,192 → 65,536; DPO 8,192 → 16,384.
- Qwen2.5-7B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2.5-7B: SFT 8,192 → 131,072; DPO 8,192 → 16,384.
- Qwen2.5-3B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2.5-3B: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2.5-1.5B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2.5-1.5B: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2-72B-Instruct / Qwen2-72B: SFT 8,192 → 32,768; DPO 8,192 → 8,192.
- Qwen2-7B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2-7B: SFT 8,192 → 131,072; DPO 8,192 → 16,384.
- Qwen2-1.5B-Instruct: SFT 8,192 → 32,768; DPO 8,192 → 16,384.
- Qwen2-1.5B: SFT 8,192 → 131,072; DPO 8,192 → 16,384.
- Llama-3.3-70B-Instruct-Reference: SFT 8,192 → 24,576; DPO 8,192 → 8,192.
- Llama-3.2-3B-Instruct: SFT 8,192 → 131,072; DPO 8,192 → 24,576.
- Llama-3.2-1B-Instruct: SFT 8,192 → 131,072; DPO 8,192 → 24,576.
- Meta-Llama-3.1-8B-Instruct-Reference: SFT 8,192 → 131,072; DPO 8,192 → 16,384.
- Meta-Llama-3.1-8B-Reference: SFT 8,192 → 131,072; DPO 8,192 → 16,384.
- Meta-Llama-3.1-70B-Instruct-Reference: SFT 8,192 → 24,576; DPO 8,192 → 8,192.
- Meta-Llama-3.1-70B-Reference: SFT 8,192 → 24,576; DPO 8,192 → 8,192.
Mistral models
- mistralai/Mistral-7B-v0.1: SFT 8,192 → 32,768; DPO 8,192 → 32,768.
- teknium/OpenHermes-2p5-Mistral-7B: SFT 8,192 → 32,768; DPO 8,192 → 32,768.
Fine-tuning: Hugging Face integrations
- Fine-tune any < 100B parameter CausalLM from Hugging Face Hub.
- Support for DPO variants such as LN-DPO, DPO+NLL, and SimPO.
- Support fine-tuning with maximum batch size.
- Public
fine-tunes/models/limits and fine-tunes/models/supported endpoints.
- Automatic filtering of sequences with no trainable tokens (e.g., if a sequence prompt is longer than the model’s context length, the completion is pushed outside the window).
Together Instant Clusters general availability
Self-service NVIDIA GPU clusters with API-first provisioning. Read more.
- New API endpoints for cluster management:
/v1/gpu_cluster: Create and manage GPU clusters./v1/shared_volume: High-performance shared storage./v1/regions: Available data center locations.
- Support for NVIDIA Blackwell (HGX B200) and Hopper (H100, H200) GPUs.
- Scale from single-node (8 GPUs) to hundreds of interconnected GPUs.
- Pre-configured with Kubernetes, Slurm, and networking components.
Serverless LoRA and dedicated endpoint support for evaluations
You can now run evaluations:Kimi-K2-Instruct-0905
Upgraded version of Moonshot’s 1 trillion parameter MoE model with enhanced performance. Read more.
- Model ID:
moonshot-ai/Kimi-K2-Instruct-0905.
DeepSeek-V3.1
Upgraded version of DeepSeek-R1-0528 and DeepSeek-V3-0324. Read more.
- Dual modes: Fast mode for quick responses; thinking mode for complex reasoning.
- 671B total parameters, with 37B active parameters.
- Model ID:
deepseek-ai/DeepSeek-V3.1.
Model deprecations
The following models have been deprecated and are no longer available:
meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo.black-forest-labs/FLUX.1-canny.meta-llama/Llama-3-8b-chat-hf.black-forest-labs/FLUX.1-redux.black-forest-labs/FLUX.1-depth.deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO.meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo.meta-llama-llama-3-3-70b-instruct-lora.Qwen/Qwen2.5-14B.meta-llama/Llama-Vision-Free.Qwen/Qwen2-72B-Instruct.google/gemma-2-27b-it.meta-llama/Meta-Llama-3-8B-Instruct.perplexity-ai/r1-1776.nvidia/Llama-3.1-Nemotron-70B-Instruct-HF.Qwen/Qwen2-VL-72B-Instruct.
GPT-OSS fine-tuning support
Fine-tune OpenAI’s open-source models to create domain-specific variants. Read more.
- Supported models:
gpt-oss-20B and gpt-oss-120B.
- Supports 16K context SFT and 8K context DPO.
OpenAI GPT-OSS models
OpenAI’s first open-weight models are now accessible through Together AI. Read more.
- Model IDs:
openai/gpt-oss-20b, openai/gpt-oss-120b.
VirtueGuard
Enterprise-grade guard model for safety monitoring with 8ms response time. Read more.
- Real-time content filtering and bias detection.
- Prompt injection protection.
- Model ID:
VirtueAI/VirtueGuard-Text-Lite.
Together Evaluations framework
Benchmarking platform using LLM-as-a-judge methodology for model performance assessment. Read more.
- Create custom LLM-as-a-judge evaluation suites for your domain.
- Supports
compare, classify, and score functionality.
- Compare models, prompts, and LLM configs; score and classify LLM outputs.
Qwen3-Coder-480B
Agentic coding model with top SWE-Bench Verified performance. Read more.
- 480B total parameters, with 35B active (MoE architecture).
- 256K context length for entire codebase handling.
- Leading SWE-Bench scores on software engineering benchmarks.
- Model ID:
Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8.
NVIDIA HGX B200 hardware support
Record-breaking serverless inference speed for DeepSeek-R1-0528 using NVIDIA’s Blackwell architecture. Read more.
- Dramatically improved throughput and lower latency.
- Same API endpoints and pricing.
- Model ID:
deepseek-ai/DeepSeek-R1.
Kimi-K2-Instruct
Moonshot AI’s 1 trillion parameter MoE model with frontier-level performance. Read more.
- Excels at tool use and multi-step tasks, with strong multilingual support.
- Strong agentic and function calling capabilities.
- Model ID:
moonshotai/Kimi-K2-Instruct.
Whisper speech-to-text APIs
High-performance audio transcription that’s 15x faster than OpenAI, with support for files over 1 GB. Read more.
- Multiple audio formats with timestamp generation.
- Speaker diarization and language detection.
- Use the
/audio/transcriptions and /audio/translations endpoints.
- Model ID:
openai/whisper-large-v3.
SOC 2 Type II compliance certification
Achieved enterprise-grade security compliance through an independent audit of security controls. Read more.
- Simplified vendor approval and procurement.
- Reduced due diligence requirements.
- Support for regulated industries.
