Together AI Release Notes

New models available for fine-tuning

You can now fine-tune the following models:

zai-org/GLM-5.2.

See Supported models for the full list.

Endpoint events in the CLI

tg beta endpoints events lists a dedicated endpoint’s audit and lifecycle events from the terminal: replica scaling, traffic shifts, status changes, and pauses across every deployment under the endpoint.

See Monitoring endpoint events and the tg beta endpoints events CLI command.

Fine-tuning limits and tokenized datasets in the CLI

Two new fine-tuning commands are available:

• tg ft model-limits <model> prints a model’s fine-tuning constraints, including sequence-length, batch-size, and LoRA rank limits.
• tg ft download-tokenized-dataset <ft_id> downloads the tokenized dataset a job trained on, so you can audit exactly what the model saw.

See the fine-tuning CLI reference.

New models

Improvements

New serverless models

The following models are now available on serverless:

thinkingmachines/Inkling-Small: 524,288 context length. Pricing: $0.50 input / $1.20 output (per 1M tokens).

Model upload progress in the console

While a remote model upload is pending or running, the Models page shows an Uploading badge on the model under My models and floats it to the top of the list. Opening the model shows a live Upload progress event log until the job finishes.

See Check upload status.

Improvements

Models page visibility filter

The Models page now lists Internal-visibility models from every project in your organization under My models, not only from the selected project. A Visibility filter lets you show Internal models, Private models, or both.

See Upload a fine-tuned model.

Project scoping in the console

Fine-tuning, Files, and Evaluations are now available in the Projects UI. Create and manage fine-tuning jobs, uploaded files, and evaluations within a project from the console, not just with project-scoped API keys.

Deprecations

Model deprecations

The following models have been deprecated and are no longer available for fine-tuning:

• nvidia/NVIDIA-Nemotron-Nano-9B-v2.
• Qwen/Qwen3-Next-80B-A3B-Instruct.
• Qwen/Qwen3-Next-80B-A3B-Thinking.
• Qwen/Qwen3-0.6B.
• Qwen/Qwen3-0.6B-Base.
• Qwen/Qwen3-1.7B.
• Qwen/Qwen3-1.7B-Base.
• Qwen/Qwen3-4B.
• Qwen/Qwen3-4B-Base.
• Qwen/Qwen3-8B.
• Qwen/Qwen3-8B-Base.
• Qwen/Qwen3-14B.
• Qwen/Qwen3-14B-Base.
• Qwen/Qwen3-32B.
• Qwen/Qwen3-30B-A3B-Base.
• Qwen/Qwen3-30B-A3B.
• Qwen/Qwen3-30B-A3B-Instruct-2507.
• Qwen/Qwen3-235B-A22B.
• Qwen/Qwen3-235B-A22B-Instruct-2507.
• Qwen/Qwen3-Coder-30B-A3B-Instruct.
• Qwen/Qwen3-Coder-480B-A35B-Instruct.
• Qwen/Qwen3-VL-8B-Instruct.
• Qwen/Qwen3-VL-32B-Instruct.
• Qwen/Qwen3-VL-30B-A3B-Instruct.
• Qwen/Qwen3-VL-235B-A22B-Instruct.
• Qwen/Qwen2.5-72B-Instruct.
• Qwen/Qwen2.5-72B.
• Qwen/Qwen2.5-32B-Instruct.
• Qwen/Qwen2.5-32B.
• Qwen/Qwen2.5-14B-Instruct.
• Qwen/Qwen2.5-14B.
• Qwen/Qwen2.5-7B-Instruct.
• Qwen/Qwen2.5-7B.
• Qwen/Qwen2.5-3B-Instruct.
• Qwen/Qwen2.5-3B.
• Qwen/Qwen2.5-1.5B-Instruct.
• Qwen/Qwen2.5-1.5B.
• Qwen/Qwen2-72B-Instruct.
• Qwen/Qwen2-72B.
• Qwen/Qwen2-7B-Instruct.
• Qwen/Qwen2-7B.
• Qwen/Qwen2-1.5B-Instruct.
• Qwen/Qwen2-1.5B.
• moonshotai/Kimi-K2.5.
• moonshotai/Kimi-K2-Thinking.
• moonshotai/Kimi-K2-Instruct-0905.
• moonshotai/Kimi-K2-Instruct.
• zai-org/GLM-5.
• zai-org/GLM-4.7.
• deepseek-ai/DeepSeek-R1-0528.
• deepseek-ai/DeepSeek-R1.
• deepseek-ai/DeepSeek-V3-0324.
• deepseek-ai/DeepSeek-V3.
• deepseek-ai/DeepSeek-V3.1-Base.
• deepseek-ai/DeepSeek-V3-Base.
• deepseek-ai/DeepSeek-R1-Distill-Llama-70B.
• deepseek-ai/DeepSeek-R1-Distill-Llama-70B-32k.
• deepseek-ai/DeepSeek-R1-Distill-Llama-70B-131k.
• deepseek-ai/DeepSeek-R1-Distill-Qwen-14B.
• deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.
• meta-llama/Llama-4-Scout-17B-16E.
• meta-llama/Llama-4-Maverick-17B-128E.
• meta-llama/Llama-3.3-70B-32k-Instruct-Reference.
• meta-llama/Llama-3.3-70B-131k-Instruct-Reference.
• meta-llama/Llama-3.2-3B-Instruct.
• meta-llama/Llama-3.2-3B.
• meta-llama/Llama-3.2-1B-Instruct.
• meta-llama/Llama-3.2-1B.
• meta-llama/Meta-Llama-3.1-8B-131k-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-8B-Reference.
• meta-llama/Meta-Llama-3.1-8B-131k-Reference.
• meta-llama/Meta-Llama-3.1-70B-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-70B-32k-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-70B-131k-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-70B-Reference.
• meta-llama/Meta-Llama-3.1-70B-32k-Reference.
• meta-llama/Meta-Llama-3.1-405B-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-405B-Reference.
• meta-llama/Meta-Llama-3.1-405B-10k-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-405B-10k-Reference.
• meta-llama/Meta-Llama-3.1-405B-8k-Instruct-Reference.
• meta-llama/Meta-Llama-3.1-405B-8k-Reference.
• meta-llama/Llama-3-8B-Instruct.
• Qwen/Qwen2.5-14B.
• Qwen/Qwen2.5-32B.
• Qwen/Qwen3-235B-A22B-Instruct-2507-FP8.
• Qwen/Qwen2-72B.
• arcee-ai/trinity-mini.
• BAAI/bge-base-en-v1.5.
• minimax/speech-2.8-turbo.
• rime-labs/rime-mist-v3.
• rime-labs/rime-mist-v3-omni.

Improvements

Deprecations

A/B variant percent updates in the CLI

tg beta endpoints update now accepts --ab-percent to change a variant’s traffic percentage in an existing A/B experiment. The flag takes percentage from or returns it to the control only; other variants stay unchanged. The control must remain at least 1%, and --percent on tg beta endpoints ab is limited to 1–99.

See Ramp the variant and the endpoints CLI reference.

Deploy replica bound inference

tg beta endpoints deploy now infers a missing replica bound: --min-replicas alone mirrors into the max (including 0 to create a deployment stopped), and --max-replicas 0 alone lowers the min to 0. On tg beta endpoints update, stopping a deployment still requires both --min-replicas 0 and --max-replicas 0. Passing a single zero bound is an error.

See the endpoints CLI reference.

CLI upgrade notices

The Together CLI now detects when a newer release is available and prints an upgrade notice at most once per day. Interactive sessions offer to run the upgrade in place, using the command that matches your install (uv, pipx, or pip). Set TOGETHER_DISABLE_VERSION_CHECK=1 to turn the check off.

See Get started.

Select dedicated endpoints in evaluations

In the evaluations console, live dedicated model inference endpoints now appear under My Endpoints in the model picker. Legacy dedicated endpoints appear under My Legacy Endpoints. Only endpoints with at least one live deployment are listed.

See Supported models.

Model deprecations

The following models have been deprecated and are no longer available on serverless:

MiniMaxAI/MiniMax-M2.7.

See Deprecations for migration options.

Python SDK realtime transcription

The Together Python SDK now includes client.beta.realtime.transcription() for streaming speech-to-text over WebSocket. Install with pip install "together[realtime]". The session reconnects with audio replay on transient drops, exposes normalized events such as TranscriptDelta and TranscriptCompleted, and supports application-level failover across endpoints with RealtimeConnectionError (code="no_healthy_workers") and session.pending_audio().

See Streaming transcription.

Fine-tuning comparison metrics filtering

The fine-tuning comparison view now includes the same Metrics filtering control as the single-job Metrics tab. Adjust Sampling rate and Step range, then select Apply to re-fetch metrics for every selected job with matching filters.

See View metrics in the dashboard.