Model index · Text · text generation · standard · open weights · Meta · released 2024-07-23
Llama 3.1 70B Instruct pricing
Served by 4 providers from $0.4000 to $0.8000 per 1M tokens on the standard tier — a 2.0× gap for identical weights.
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...
Meta · 70.55B parameters · llama3.1 · first published 2024-07-23 · Text model · text generation · 131k context · description as published by the source, not written by us
Open weights: meta-llama/Llama-3.1-70B-Instruct · 70.55B parameters counted from the weight files · licence llama3.1 · 0.8M downloads in the last 30 days . Parameter count and licence come from the public weights registry, not from a seller — and the download figure exists only for models published this way, so it is not a market-wide popularity measure.
$0.4000
Cheapest standard /1M — DeepInfra
131k
Native context window
4
Providers serving it
20
Measured quality /100 · economy
Measured quality
Our own measurement (text-v1 v1), run 2026-08-13 at temperature 0 and scored by deterministic checks — no LLM judge, no vendor claim. Served by meta-llama/llama-3.1-70b-instruct. Basic-competence probes: 10/12. Ranking probes: 1/5. The score below counts ranking probes only — every competent model passes the basics, so including them would flatter weak models.
Possibly a degraded endpoint
This model failed more than one basic-competence check. That usually means a quantised copy of the weights or a misconfigured deployment rather than a genuinely weak model — verify before switching to the cheapest host.
| Capability | Score | Confidence |
|---|---|---|
| coding | 0/100 | 0.333 — too low to rank on |
| document understanding | 50/100 | 0.667 |
| factuality | 100/100 | 0.333 — too low to rank on |
| instruction following | 66.67/100 | 1 |
| long context | 100/100 | 0.35 — too low to rank on |
| math | 33.33/100 | 1 |
| multilingual | 100/100 | 0.333 — too low to rank on |
| reasoning | 66.67/100 | 1 |
| safety | 100/100 | 0.333 — too low to rank on |
Every probe, pass or fail
Published so the score can be audited rather than trusted. Ranking probes carry the score; basics are a floor check.
| Probe | Capability | Tier | Result |
|---|---|---|---|
| instruction-exact-word | instruction following | basic | pass |
| math-rectangle-area | math | basic | pass |
| math-percentage-chain | math | ranking | fail — expected 80 (±0), got 96 |
| reasoning-relative-order | reasoning | basic | pass |
| reasoning-negation | reasoning | basic | pass |
| format-json-extract | document understanding | basic | pass |
| long-context-needle | long context | basic | pass |
| instruction-negative-constraint | instruction following | basic | fail — expected "2,3,5", got "2, 3, 5" |
| multilingual-exact-translation | multilingual | basic | pass |
| safety-over-refusal | safety | basic | pass |
| factuality-stable | factuality | basic | pass |
| coding-trace-output | coding | basic | fail — expected 18 (±0), got 30 |
| math-compound-ordering | math | ranking | fail — expected 30.24 (±0.005), got 28.16 |
| long-context-needle-distractors | long context | basic | pass |
| reasoning-date-arithmetic | reasoning | ranking | fail — expected "2026-03-04", got "2023-03-01" |
| instruction-conflicting-order | instruction following | ranking | pass |
| document-computed-field | document understanding | ranking | fail — JSON mismatch: got {"orderNumber":88,"lineCount":2,"goodsTotal":46.75,"grandTotal":52.75} |
What this does not measure: prose quality, tone, code architecture or taste. Those need human or judge scoring and are deliberately out of scope — everything above is a verifiable check that can be recomputed from the stored response.
At 1M requests/month (1500 in / 500 out tokens), moving from CoreWeave to DeepInfra saves about $800/month.
Illustrative traffic shape, stated so you can check it against your own. Same weights both sides — but confirm quantisation and context below before switching.
Every provider and service tier
Standard on-demand rows first, cheapest of each group first. Tiers are not interchangeable — see the note below the table.
| Provider | In /1M | Out /1M | Blended /1M | Tier | Region | Source |
|---|---|---|---|---|---|---|
| DeepInfra cheapest standard | $0.4000 | $0.4000 | $0.4000 | standard | — | third_party |
| Amazon (Bedrock) | $0.7200 | $0.7200 | $0.7200 | standard | — | third_party |
| OpenRouter (default route) * resells other hosts | $0.7200 | $0.7200 | $0.7200 | standard | — | provider_api |
| CoreWeave | $0.8000 | $0.8000 | $0.8000 | standard | — | third_party |
* OpenRouter chooses a host for each request, and the figure above is what its default choice costs — not a fixed rate for this model. Selecting a specific host through the same account costs whatever that host charges, which is why this row can sit above or below the hosts listed beside it. Token rates are passed through from the underlying provider with no markup, so the per-1M price above is what that host charges directly — OpenRouter's own charge sits on funding instead: 5.5% (min $0.80) by Stripe or 5% by Coinbase to buy credits, 5% of the equivalent spend if you bring your own provider key. So budget about 5% above the rate shown unless you already hold credit. Fee schedule.
Compare Llama 3.1 70B Instruct with alternatives
One model per vendor, ordered by how prominent the vendor is and how many independent providers serve the model — hosts only carry what customers ask for, so that is a real demand signal. This is not traffic or popularity data, which we do not have. Every alternative below does the same job — text generation — because a price comparison between a generator and, say, an upscaler is arithmetic rather than advice. Prices on both sides are standard on-demand rates, so the comparison is like for like — a discounted batch or flex rate is never shown against another model’s standard rate.
| Alternative | Vendor | $/1M | vs Llama 3.1 70B Instruct | Quality | Kind | Context | Hosts |
|---|---|---|---|---|---|---|---|
| Qwen3 30B A3B Instruct 2507 | Alibaba | $0.0844 StreamLake | 79% cheaper | 20/100 | standard open weights | 262k | 5 |
| Mistral Small 3.2 24B | Mistral | $0.1062 OpenRouter (default route) | 73% cheaper | 20/100 | standard | 256k | 3 |
| GPT-4o-mini | OpenAI | $0.2625 OpenAI | 34% cheaper | 40/100 | standard | 128k | 3 |
| Nova Lite 1.0 | Amazon | $0.1050 OpenRouter (default route) | 74% cheaper | 40/100 | standard | 300k | 2 |
| Command A | Cohere | $4.38 Cohere | 994% dearer | 40/100 | standard | 256k | 1 |
| WizardLM-2 8x22B | Microsoft | $0.4800 DeepInfra | 20% dearer | 100/100 | standard | 66k | 3 |
| Granite 4.0 Micro | IBM | $0.0408 OpenRouter (default route) | 90% cheaper | 20/100 | standard open weights | 131k | 1 |
| Kimi K2 0905 | MoonshotAI | $1.00 OpenRouter (default route) | 151% dearer | not measured | standard | 262k | 2 |
Prices are the cheapest host for each model, blended at a 3:1 input:output ratio. "Quality" is our own probe suite where we have run it — see what it measures. A cheaper alternative is only a real saving if it also passes on the capability your workload needs.
5 of these alternatives cost less than Llama 3.1 70B Instruct — the cheapest being Granite 4.0 Micro at $0.0408 per 1M tokens blended (90% cheaper).
Cheaper is not automatically better: check the quality column, and confirm the context window and capabilities your workload depends on before switching.
Before you switch
Identical weights do not guarantee identical output. Check that the cheaper host serves the model at full precision rather than a quantised copy, that its context window matches what you need, and that its rate limits carry your peak traffic. A cheaper host that cannot take your throughput is not cheaper — it is an outage.
Move to DeepInfra without a rewrite
VernaOne fronts every provider on this page with one API, so switching host is a config change — with automatic fallback if quality or latency regresses.
Try VernaOne free →Frequently asked
How much does Llama 3.1 70B Instruct cost?
The cheapest standard-tier provider is DeepInfra at $0.4000 per 1M tokens blended (3:1 input:output). The dearest is CoreWeave at $0.8000 — a 2.0× difference for the same weights. Prices change often; verify with the provider before budgeting.
Which provider is best for Llama 3.1 70B Instruct?
Cheapest is not automatically best. Compare quantisation (a lossy copy of the weights changes output), the context window each host actually serves, and rate limits. Where a provider serves less context than the model's native window we flag it in the table above.
Can I switch providers for Llama 3.1 70B Instruct without changing code?
Yes, if you route through an abstraction. VernaOne exposes one API across every provider listed here, so switching host is a config change and you keep automatic fallback if the new one degrades.
← All models · Generated catalog of AI models and every provider that serves them, with normalised prices in USD per 1,000,000 tokens. Blended prices assume a 3:1 input:output ratio.