--- title: Llama 2 Chat (7B)—Performance and quality report canonical: https://telnyx.com/llm-library/llama-2-7b-chat-hf --- # Llama 2 Chat (7B)—Performance and quality report [Start building](https://telnyx.com/sign-up) ### Ask AI * [![GPT](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/chatgpt_icon-ea55e09d.jpeg?width=92&format=webp)](https://chat.openai.com/?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Claude](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/claude_icon-c7c8eef3.webp?width=92&format=webp)](https://claude.ai/new?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Perplexity](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/perplexity_icon-cd5b74f1.webp?width=92&format=webp)](https://www.perplexity.ai/search?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Gemini](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/gemini_icon-4ff1cbf4.webp?width=92&format=webp)](https://www.google.com/search?udm=50&aep=11&q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Grok](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/grok_icon-e588e5bd.webp?width=92&format=webp)](https://x.com/i/grok?text=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) # Llama 2 Chat 7B llama-2-7b-chat-hf llama-2-13b-chat-hf llama-2-70b-chat-hf llava-v1.6-mistral-7b-hf Meta-Llama-3-8B-Instruct Meta-Llama-3-70B-Instruct Meta-Llama-3.1-70B-Instruct mistral-7b-instruct-v0.1 Mistral-7b-Instruct-v0.2 mixtral-8x7b-instruct-v0.1 Nous-Hermes-2-Mistral-7B-DPO Nous-Hermes-2-Mixtral-8x7b-DPO zephyr-7b-beta ultravox-v0\_4\_1-llama-3\_1-8b Llama-3.3-70B-Instruct Llama-Guard-3-1B claude-sonnet-4-20250514 claude-haiku-4-5 claude-opus-4-6 claude-3-7-sonnet-latest llama-4-scout-17b-16e-instruct gemini-2.0-flash gemini-2.5-flash gemini-2.5-flash-lite gpt-oss-120b Kimi-K2.5 Kimi-K2-Instruct Meta-Llama-3.1-8B-Instruct MiniMax-M2.5 llama-3.3-70b-versatile llama-4-maverick-17b-128e-instruct GLM-5 gpt-4-turbo-preview o1-mini o1-preview gpt-4-32k-0314 gpt-4.1 gpt-4.1-mini Qwen3-235B-A22B gpt-4o-mini gpt-5 gpt-5-mini gpt-5.1 gpt-5.2 [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about Meta trained the 7B chat variant on 2 trillion tokens and aligned it using over [1 million human annotations](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) through a two-stage process of supervised fine-tuning followed by RLHF with rejection sampling and PPO. The safety training was notably aggressive, leading to community discussion about over-refusal, but it established the template for open-weight chat model alignment at commercial scale. LicenseLLAMA 2 Community License Context window ## Explore Our LLM Library Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project. ### DeepSeek-V4-Flash-0731 OrganizationTelnyx Model NameDeepSeek-V4-Flash-0731 Taskstext generation Languages SupportedEnglish Context Length1,048,576 Parameters284.0B Model Tierunlisted Licensemit ### DeepSeek-V4.1-Flash ### gemma-2b-it ### gemma-4-26B-A4B-it ### gemma-4-31B-it ### Llama-3.3-70B-Instruct ### Meta-Llama-3.1-70B-Instruct ### Meta-Llama-3.1-8B-Instruct ### MiniMax-M2.7 ### MiniMax-M3-MXFP8 ### Kimi-K2.5 ### Kimi-K2.6 ### Kimi-K3 ### NVIDIA-NemotronLabs-VoiceChat-11B ### gpt-oss-120b ### Laguna-S-2.1 ### Qwen3-235B-A22B ### Qwen3.8-27B ### GLM-5.1-FP8 ### GLM-5.2 ### GLM-5.3 ### GLM-5.3-Flash ### claude-haiku-4-5 ### gemini-2.5-flash ### gemini-3.7-flash ### gpt-oss-120b ### gpt-4.1 ### gpt-4o ### gpt-4o-mini ### gpt-5 ### gpt-5.1 ### gpt-5.2 ### gpt-5.4 ### gpt-5.4-mini ### gpt-5.6-luna ### gpt-5.6-sol ### gpt-6-luna ### gpt-live-1 HOW IT WORKS ## Selecting LLMs for Voice AI [GET Available Models](https://developers.telnyx.com/docs/inference/models) ### Create an account Sign-up to the Telnyx platform to get access to our suite of intuitive AI tools. ### Choose Llama 2 Chat 7B In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Llama 2 Chat 7B from the dropdown. ### Enter your API key If you’re using a proprietary model, you’ll need your API key before prompting the LLM. Our vast selection of OS models allow you to get started right away. ### Prompt the LLM Get responses from an LLM in minutes, and scale with our [OpenAI-compatible APIs](https://developers.telnyx.com/docs/inference/getting-started "Inference API Quickstart"). Step 1/4 **RESOURCES** ## Get started Check out our helpful tools to help get you started. * ### Test in the portal Easily browse and select your preferred model in the AI Playground. [Test today](https://portal.telnyx.com/#/ai/assistants) ## Sign up and start building [Sign up](https://telnyx.com/sign-up)[Contact sales](https://telnyx.com/contact-us) ## faqs ### What is Llama 2 7B Chat? Llama 2 7B Chat is Meta's instruction-tuned conversational model with 7 billion parameters, fine-tuned for dialogue using RLHF. It was released in July 2023 and is available on [Hugging Face](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) under Meta's community license. ### How to use Llama 2 7B Chat HF? Llama 2 7B Chat can be loaded through the Hugging Face Transformers library or deployed locally with Ollama and llama.cpp. For production use, provide API access without managing GPU infrastructure. (in thousands) 4096 ## Use cases for Llama 2 Chat 7B 1. **Conversational AI prototyping:** As Meta's smallest RLHF-tuned chat model with a permissive commercial license, it enables rapid development of conversational agents on single-GPU hardware. 2. **Fine-tuning base for domain chat:** Trained with over 1 million human annotations for RLHF, it provides a strong safety-aligned foundation for custom chatbots in healthcare, education, and support. 3. **On-device dialogue:** At 7B parameters, it runs with quantization on consumer hardware and mobile devices for offline conversational applications. ## Quality Arena Elo1037 MMLU45.8 MT Bench6.27 Llama 2 7B Chat scores 45.3% on MMLU (5-shot), placing it well below Llama 3 8B Instruct (67.4%) on the same sheet despite similar parameter counts. The 22-point gap reflects the generational improvement from 2T to 15T training tokens between the Llama 2 and Llama 3 families. Within the Llama 2 lineup, the 7B trails the 13B (54.8%) by about 10 points. **Code Llama 70B Instruct** 1042 **Gemma 7B IT** 1038 **Llama 2 Chat 7B** 1037 **Nous Hermes 2 Mistral 7B** 1010 **Mistral 7B Instruct v0.1** 1008 ## pricing The cost of running the model with Telnyx Inference is $0.0002 per 1,000 tokens. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens long, would cost $200. ## What's Twitter saying? * Developers praise Llama 2 7B Chat for outperforming most open-source chat models in benchmarks like Arena Elo (1037) and MMLU (45.8), with strong conversation understanding and translation skills, though it needs fine-tuning for complex tasks. * Tech commentators note its coding strengths, as Llama 2 Chat generated working Python web scraping code out-of-the-box where models like CodeWhisperer failed, but criticize the RLHF-tuned chat version as overly censored and unhelpful. * Community users report setup frustrations on platforms like GitHub and Hugging Face, with weird local responses compared to HF API outputs, often due to tokenizer issues or hardware limits. | Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License | | ------------ | ----------------------------------------------------------------------- | ---------------- | ------------------- | -------------- | ---------- | ---------- | ----------------- | | Telnyx | DeepSeek-V4-Flash-0731 | text generation | English | 1,048,576 | 284.0B | unlisted | mit | | Telnyx | DeepSeek-V4.1-Flash | text generation | English | 1,048,576 | 552.0B | small | mit | | google | [gemma-2b-it](https://telnyx.com/llm-library/gemma-2b-it) | text generation | English | 8,192 | 2.5B | small | gemma | | google | gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | | google | gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | | meta-llama | [Llama-3.3-70B-Instruct](https://telnyx.com/llm-library/llama-3.3-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 | | meta-llama | [Meta-Llama-3.1-70B-Instruct](https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 | | meta-llama | [Meta-Llama-3.1-8B-Instruct](https://telnyx.com/llm-library/meta-llama-3.1-8b-instruct) | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 | | minimaxai | MiniMax-M2.7 | text generation | Multilingual | 200,000 | 0 | large | minimaxai | | minimaxai | MiniMax-M3-MXFP8 | text generation | Multilingual | 1,000,000 | 428.0B | small | minimax-community | | moonshotai | [Kimi-K2.5](https://telnyx.com/llm-library/kimi-k2.5) | text generation | Multilingual | 256,000 | 1.0T | large | modified-mit | | moonshotai | Kimi-K2.6 | text generation | Multilingual | 262,144 | 1.0T | medium | modified-mit | | moonshotai | Kimi-K3 | text generation | English | 1,000,000 | 2.8M | large | MIT Modified | | nvidia | NVIDIA-NemotronLabs-VoiceChat-11B | speech to-speech | English | 8,192 | 11.1B | medium | openmdw-1.1 | | openai | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 131,072 | 117.0B | small | apache-2.0 | | poolside | Laguna-S-2.1 | text generation | English | 1,048,576 | 117.6B | unlisted | OpenMDW-1.1 | | Qwen | [Qwen3-235B-A22B](https://telnyx.com/llm-library/qwen3-235b-a22b) | text generation | English | 32,768 | 235.1B | large | apache-2.0 | | Telnyx | Qwen3.8-27B | text generation | English | 262,144 | 27.0B | small | Apache 2.0 | | zai-org | GLM-5.1-FP8 | text generation | Multilingual | 202,752 | 753.9B | large | mit | | zai-org | GLM-5.2 | text generation | English | 1,000,000 | 753.9B | medium | MIT | | Telnyx | GLM-5.3 | text generation | English | 1,048,576 | 753.0B | medium | glm-5.3 | | zai-org | GLM-5.3-Flash | text generation | English | 1,048,576 | 320.0B | small | mit | | anthropic | [claude-haiku-4-5](https://telnyx.com/llm-library/claude-haiku-4-5) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | [gemini-2.5-flash](https://telnyx.com/llm-library/gemini-2.5-flash) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | | groq | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-4.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o](https://telnyx.com/llm-library/gpt-4-omni) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o-mini](https://telnyx.com/llm-library/gpt-4o-mini) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-5.2](https://telnyx.com/llm-library/gpt-5.2) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4-mini | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-luna | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-sol | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-6-luna | text generation | Multilingual | 1,050,000 | 0 | large | openai | | openai | gpt-live-1 | speech to-speech | Multilingual | 400,000 | 0 | large | openai | * ### Explore the docs Don’t wait to scale, start today with our public API endpoints. [Get started](https://developers.telnyx.com/api-reference/assistants/list-assistants) * ### Stay up to date Keep an eye on our AI changelog so you don't miss a beat. [See updates](https://telnyx.com/release-notes) hosted inference platforms ### Is Llama 2 7B good? Llama 2 7B was a strong model at launch, outperforming many open-source alternatives at the 7B scale. It has since been surpassed by Llama 3 and Mistral 7B on most benchmarks, making those [newer alternatives](https://telnyx.com/llm-library/llama-2-13b-chat-hf) better choices for new projects. ### Can I use Llama 2 for free? Yes, Llama 2 is released under Meta's community license, which permits free use for research and commercial applications with fewer than 700 million monthly active users. Weights are available on [Hugging Face](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). ### How much RAM does Llama 2 7B need? Llama 2 7B requires approximately 14GB of RAM for full-precision inference, or 4-6GB with 4-bit quantization. This makes it runnable on [consumer GPUs and even some CPU-only setups](https://telnyx.com/llm-library/llama-2-7b-chat-hf) with quantized formats. ### Is Llama 2 7B better than GPT-3.5? Llama 2 7B is generally comparable to GPT-3.5 on straightforward tasks but falls short on complex reasoning. For cost-sensitive [inference workloads](https://telnyx.com/llm-library/llama-2-7b-chat-hf), the self-hostable nature of Llama 2 can make it more economical than API-based alternatives. Llama 2 Chat (7B)—Performance and quality report