--- title: "Meta: Llama 3.1 70B Instruct — price, capability and what beats it · Undominated.ai" canonical: https://undominated.ai/models/meta-llama__llama-3.1-70b-instruct/ description: "Meta: Llama 3.1 70B Instruct: $0.720/M in, $0.720/M out. Independent capability scores, and alternatives compared under recorded workload and capability requirements." --- # Meta: Llama 3.1 70B Instruct — price, capability and what beats it · Undominated.ai > Meta: Llama 3.1 70B Instruct: $0.720/M in, $0.720/M out. Independent capability scores, and alternatives compared under recorded workload and capability requirements. [Check base rates for this workload](/check/meta-llama__llama-3.1-70b-instruct/) [Cheaper alternatives](/alternatives/meta-llama__llama-3.1-70b-instruct/) [Family](/families/llama/) [Compare models](https://undominated.ai/compare/?models=meta-llama%2Fllama-3.1-70b-instruct) Comparison context Choose up to three standard models in Compare. Requirements and prompt length apply in Compare. This page keeps its stated price basis and workload controls. MODEL PROOF # Llama 3.1 70B Instruct Meta · 23 Jul 2024 Balanced Summarise Chat Code gen Agentic BETTER VALUE OPTION ## A stored alternative has an equal-or-higher measured score and an equal-or-lower price. **$0.720** / 1M tokens Balanced · 3 tokens in per 1 out deal-only price: No offer passes the like-for-like test, so this price is a fallback: precision not disclosed. · precision not disclosed: The seller whose offer sets this price does not declare its serving precision. It is not read as full precision. · Serving precision differs between offers. [MiMo-V2.6-Pro](/models/xiaomi__mimo-v2.6-pro/) · Δ score 230.1 points · 25% lower measured price · $0.540 / 1M tokens [Mercury 2](/models/inception__mercury-2/) is a further stored option with these losses: 131K → 128K context Evidence as of 06 Oct 2026 [openrouter/models](https://openrouter.ai/api/v1/models) [LMArena · cc-by-4.0](https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset) Independent LMArena score 1,260.9 ± 3.71 · 55,240 votes Context window 131.07K Maximum output 16.38K Input modalities text Output modalities text Published input price $0.720 / 1M tokens Published output price $0.720 / 1M tokens Pricing kind fixed Inspect [complete billing conditions](#billing-details) and endpoint terms below. Price from Amazon Bedrock: no serving offer passes the like-for-like test, so this is a fallback. deal-only price precision not disclosed The seller whose offer sets this price does not declare its serving precision. It is not read as full precision. Cheaper right now: $0.400/M at DeepInfra — delivery tier (turbo) It does not pass the like-for-like test, so it never sets a rank. Every price dimension · USD per million tokens | Input | $0.720 /M | | --- | --- | | Output | $0.720 /M | | Cached input | Unknown /M | | Cache write | Unknown /M | | Cache write 1h | Unknown /M | | Reasoning | Unknown /M | ### Who sells it The same weights, different shops. Cheapest is not like-for-like when serving precision differs. 2 provider offers · rates, limits and conditions Rates are USD per million tokens. Endpoint terms can differ even when the quoted price and precision match. A listed parameter is a provider declaration, not a task-success test. | Seller | Input | Output | Precision | Uptime (1d) | | --- | --- | --- | --- | --- | | Seller DeepInfra deepinfra/turbo Endpoint terms Cached input /M Unknown Context limit 131,072 tokens Output limit 16,384 tokens Tools Listed by endpoint Reasoning Not listed by endpoint Promotional discount None reported Uptime · last 30 minutes 99.95% | Input $0.400 | Output $0.400 | Precision fp8 | Uptime (1d) 99.66% | | Seller Amazon Bedrock amazon-bedrock Endpoint terms Cached input /M Unknown Context limit 131,072 tokens Output limit 8,192 tokens Tools Not listed by endpoint Reasoning Not listed by endpoint Promotional discount None reported Uptime · last 30 minutes 100.00% | Input $0.720 | Output $0.720 | Precision undeclared | Uptime (1d) 99.98% | ### Independent scores | LMArena Elo | 1260.9 default | | --- | --- | LMArena Elo under CC BY 4.0. Absence of another board is not a score of zero. ### Capability | Context window | 131K | | --- | --- | | Max output | 16K | | Input modes | text | | Tool use | yes | | Reasoning | no | | Knowledge cutoff | 2023-12-31 | | Open weights | Unknown | ### Provenance | Price source | [openrouter.ai](https://openrouter.ai/api/v1/models) | | --- | --- | | Fetched | 2026-10-06 | | Quality data | verified | Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong... ## Questions this page answers What does Llama 3.1 70B Instruct cost? $0.720/M in, $0.720/M out in this catalogue, as of the fetch date on this page. That is the API row, not a subscription. Does Llama 3.1 70B Instruct have an independent quality score? Llama 3.1 70B Instruct has an LMArena score in this catalogue. What beats Llama 3.1 70B Instruct? MiMo-V2.6-Pro has an equal-or-higher measured score and an equal-or-lower price. Does this page use Artificial Analysis scores for Llama 3.1 70B Instruct? No. Artificial Analysis figures are not published here. Quality on this page is LMArena Elo where a score exists; otherwise the row is unrated. MODEL MONUMENT ## Save or share this proof Download proof card · 1200 × 630 Preview and more sharing options Meta: Llama 3.1 70B Instruct. Balanced workload. Better value option available. Evidence as of 06 Oct 2026. Copy model link Download square card · 1080 × 1080 Copy landscape image Share landscape card ## Related - Both better and cheaper [MiMo-V2.6-Pro](/models/xiaomi__mimo-v2.6-pro/) - Higher score and cheaper, with a named trade [Mercury 2](/models/inception__mercury-2/) - Nearby on the capability axis [Mistral Large 2407](/models/mistralai__mistral-large-2407/) - Same vendor [Llama 3.3 70B Instruct](/models/meta-llama__llama-3.3-70b-instruct/) - Meta models [Meta](/providers/meta/) - Where this model appears [spreads](/spreads/) - Nearby on the capability axis [Mistral Small 3](/models/mistralai__mistral-small-24b-instruct-2501/) - Same vendor [Llama 3.1 8B Instruct](/models/meta-llama__llama-3.1-8b-instruct/) - Where this model appears [traps](/traps/) - Nearby on the capability axis [Qwen2.5 72B Instruct](/models/qwen__qwen-2.5-72b-instruct/) - Same vendor [Llama 3.2 3B Instruct](/models/meta-llama__llama-3.2-3b-instruct/) - Where this model appears [significance](/significance/) ## Badge The verdict as one SVG, for a README or a docs page. It states this model's dominance status at the balanced workload on the LMArena lens, with the date it was computed, and it is rebuilt with the catalogue — so it changes when the verdict changes, including to one you would rather it did not. Markdown Copy [![Llama 3.1 70B Instruct — Undominated.ai dominance verdict](https://undominated.ai/badge/meta-llama__llama-3.1-70b-instruct.svg)](https://undominated.ai/models/meta-llama__llama-3.1-70b-instruct/) HTML Copy Llama 3.1 70B Instruct — Undominated.ai dominance verdict Direct file: [https://undominated.ai/badge/meta-llama__llama-3.1-70b-instruct.svg](https://undominated.ai/badge/meta-llama__llama-3.1-70b-instruct.svg) — a static SVG, written by scripts/build-badges.mjs on every build, so the copy you embed is never older than the last deploy. ## Continue your investigation - [Explore model families](/families/) - [Compare a shortlist](/compare/) - [Check a model](/check/) - [Understand the evidence](/methodology/)