Llama 3.3 70B in 2026: Benchmarks, API Providers, Pricing, and Is It Still Worth Running?

TokenMix Research Lab · 2026-04-05

Llama 3.3 70B in 2026: Benchmarks, API Providers, Pricing, and Is It Still Worth Running?

Llama 3.3 70B is the most widely deployed open-source LLM on third-party APIs — available through 20+ providers at prices ranging from $0.05/M ([Groq](https://tokenmix.ai/blog/groq-api-pricing)) to $0.88/M (Together). It benchmarks at 72% on SWE-bench with 88% HumanEval, rivaling GPT-4o while costing 80-95% less. But Llama 4 Scout and newer models are closing in. This guide ranks every Llama 3.3 70B provider by price and speed, compares its benchmarks against current-gen models, and tells you when it's still the right choice. Data from [Meta's official Llama page](https://www.llama.com/models/llama-3/), [Artificial Analysis](https://artificialanalysis.ai/models/llama-3-3-instruct-70b), and [TokenMix.ai](https://tokenmix.ai), April 2026.

Table of Contents

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Llama 3.3 70B Quick Specs and Benchmark Summary

| Spec | Value | | ------------------- | ---------------------------- | | Parameters | 70 billion | | Architecture | Dense transformer | | Context window | 128K tokens | | Training data cutoff| December 2024 | | License | Llama 3.3 Community License | | HumanEval | 88.4% | | SWE-bench | ~72% | | MMLU | ~86% | | Best provider speed | 315 tokens/sec (Groq) |

**Why Llama 3.3 70B still matters:** It's the sweet spot of open-source LLMs — large enough for production quality, small enough to run on consumer hardware (quantized), and available through more API providers than any other model.

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Llama 3.3 70B API Pricing: Every Provider Compared

Prices per 1M tokens, April 2026:

| Provider | Input/M | Output/M | Speed (TPS) | Latency (TTFT) | Free Tier | | ---------------------- | ------- | -------- | ----------- | --------------- | --------- | | **Groq** | $0.59 | $0.79 | 315 | 0.8s | Yes | | **DeepInfra (FP8)** | $0.35 | $0.35 | 27 | 1.2s | Credits | | **Together AI** | $0.88 | $0.88 | 45 | 1.0s | $1 credit | | **Fireworks** | $0.70 | $0.70 | 50 | 0.6s | Credits | | **Nebius (Fast)** | $0.42 | $0.42 | 80 | 0.9s | No | | **SambaNova** | $0.50 | $0.50 | 294 | 1.5s | Yes | | **Hyperbolic** | $0.40 | $0.40 | 35 | 1.5s | Free tier | | **Cloudflare** | Free* | Free* | 30 | 2.0s | Yes | | **TokenMix.ai** | $0.56 | $0.75 | Varies | Varies | No fee |

*Cloudflare Workers AI free tier: 10K neurons/day limit.

**Price winner:** DeepInfra at $0.35/$0.35 — cheapest paid option. **Speed winner:** Groq at 315 TPS — 6-12x faster than most competitors. **Free winner:** Cloudflare Workers AI — genuinely free with daily limits. **Best balance:** [TokenMix.ai](https://tokenmix.ai) — routes to the cheapest/fastest available provider automatically with failover.

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Llama 3.3 70B Benchmark Performance: SWE-bench, HumanEval, MMLU

| Benchmark | Llama 3.3 70B | GPT-4o | GPT-5.4 Mini | Claude Haiku 4.5 | DeepSeek V4 | | ------------ | ------------- | -------- | ------------ | ---------------- | ----------- | | SWE-bench | ~72% | ~72% | ~72% | ~68% | 81% | | HumanEval | 88.4% | 90% | 87% | 82% | 92% | | MMLU | ~86% | ~88% | ~85% | ~82% | 88% | | Context | 128K | 128K | 400K | 200K | 1M |

**Key takeaway:** Llama 3.3 70B matches GPT-4o across the board. It's not frontier-class ([DeepSeek V4](https://tokenmix.ai/blog/deepseek-api-pricing) and GPT-5.4 are ahead), but for the price — $0.35-$0.88/M vs GPT-4o's $2.50/$10 — it's exceptional value.

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Llama 3.3 70B vs Llama 4 Scout: Should You Upgrade?

[Llama 4 Scout](https://tokenmix.ai/blog/llama-4-vs-llama-3-3) is Meta's newer MoE model (17B x 16 experts). How does it compare?

| Metric | Llama 3.3 70B | Llama 4 Scout | | ------------------- | ------------- | ------------------- | | Architecture | Dense 70B | MoE 17B x 16 (272B total, 17B active) | | Active params | 70B | 17B | | Context | 128K | 512K | | Speed (Groq) | 315 TPS | 594 TPS | | Price (Groq) | $0.59/$0.79 | $0.11/$0.34 | | SWE-bench | ~72% | ~68% | | HumanEval | 88.4% | ~84% |

**Llama 3.3 70B is still better for quality.** Scout is faster and cheaper but scores 4-5 points lower on coding benchmarks. Choose Scout for speed/cost-sensitive tasks, stay on 3.3 70B for quality-sensitive work.

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Llama 3.3 70B vs GPT-4o vs Claude Haiku vs DeepSeek V4

Complete cost/quality comparison:

| Model | Cheapest API Price | Output/M | SWE-bench | Best For | | ------------------ | ------------------ | -------- | --------- | --------------------------- | | **Llama 3.3 70B** | $0.35 (DeepInfra) | $0.35 | 72% | Open-source, self-hostable | | GPT-4o | $2.50 | $10.00 | 72% | OpenAI ecosystem | | Claude Haiku 4.5 | $1.00 | $5.00 | 68% | Anthropic ecosystem | | DeepSeek V4 | $0.30 | $0.50 | 81% | Cheapest frontier model | | Grok 4.1 Fast | $0.20 | $0.50 | 70% | Largest context (2M) |

**Llama 3.3 70B vs GPT-4o:** Same quality, 86-96% cheaper. The trade-off: no official support, variable quality across providers, 128K vs 128K context (same).

**Llama 3.3 70B vs DeepSeek V4:** DeepSeek is slightly cheaper ($0.30 vs $0.35 at DeepInfra) and significantly better on benchmarks (81% vs 72%). DeepSeek wins on both price and quality — Llama's advantage is being open-source and self-hostable.

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Llama 3.3 70B Speed: Groq vs Together vs Fireworks

For latency-sensitive applications, provider choice matters as much as model choice:

| Provider | Output Speed (TPS) | Time to First Token | Best For | | ------------ | ------------------- | ------------------- | ----------------------- | | **Groq** | 315.6 | 0.8s | Real-time chat, voice | | SambaNova | 294.1 | 1.5s | High throughput | | Amazon Bedrock| 189.8 | 1.1s | AWS integration | | Nebius Fast | 80 | 0.9s | EU data residency | | Fireworks | 50 | 0.6s | Lowest TTFT | | Together | 45 | 1.0s | Fine-tuning support | | DeepInfra | 27 | 1.2s | Cheapest price |

**Groq is 12x faster than DeepInfra** — but costs 1.7x more ($0.59 vs $0.35 input). For user-facing chat, the speed difference is worth the premium. For batch processing, DeepInfra's price wins.

Data: [Artificial Analysis Llama 3.3 70B provider benchmarks](https://artificialanalysis.ai/models/llama-3-3-instruct-70b/providers)

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Running Llama 3.3 70B Locally: Hardware Requirements

| Quantization | VRAM Required | Quality Loss | Hardware Example | | ------------ | ------------- | ------------ | --------------------------- | | FP16 (full) | ~140 GB | None | 2x A100 80GB | | INT8 | ~70 GB | Minimal | 1x A100 80GB | | GGUF Q4_K_M | ~40 GB | Small | 1x A6000 48GB or Mac M4 Max| | GGUF Q3_K_S | ~30 GB | Moderate | Mac M4 Pro 36GB |

**Self-hosting math:** An A100 80GB cloud instance costs ~$1.50-$2.00/hour. If you're processing >50M tokens/month, self-hosting becomes cheaper than API providers. Below that, APIs win on convenience.

For most teams, **API access through providers is simpler.** Use [TokenMix.ai](https://tokenmix.ai) to access Llama 3.3 70B alongside 155+ other models — automatically routing to the cheapest or fastest provider.

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How to Choose the Right Llama 3.3 70B Provider

| Your Priority | Recommended Provider | Why | | ------------------------------ | ----------------------- | ------------------------------------ | | Fastest inference | **Groq** ($0.59/$0.79) | 315 TPS, 12x faster than average | | Cheapest price | DeepInfra ($0.35/$0.35) | Lowest per-token cost | | Free prototyping | Cloudflare Workers AI | Genuinely free, 10K neurons/day | | AWS integration | Amazon Bedrock | IAM, VPC, compliance | | Fine-tuning support | Together AI ($0.88/$0.88)| Best fine-tuning infrastructure | | Multi-model with failover | **TokenMix.ai** | Route to best provider automatically | | Self-hosting, full control | Run locally (GGUF) | Free after hardware cost | | EU data residency | Nebius ($0.42/$0.42) | EU-based infrastructure |

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Conclusion

Llama 3.3 70B remains the most practical open-source LLM in 2026. It matches GPT-4o quality at 86-96% lower cost across 20+ API providers. Groq runs it at 315 tokens/sec — faster than any proprietary model API. DeepInfra offers it at $0.35/M — cheaper than everything except DeepSeek V4.

The competitive pressure is real: DeepSeek V4 is both cheaper and better on benchmarks, and Llama 4 Scout is faster and cheaper (though lower quality). Llama 3.3 70B's advantage is the combination of strong quality, massive provider ecosystem, open weights for self-hosting, and a proven production track record.

For most teams, the best approach is accessing Llama 3.3 70B through a unified gateway like [TokenMix.ai](https://tokenmix.ai) — automatically routing to the cheapest or fastest provider while maintaining access to 155+ other models for tasks where Llama falls short.

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FAQ

How much does Llama 3.3 70B API cost?

Ranges from $0.35/M (DeepInfra) to $0.88/M (Together) depending on provider. Groq charges $0.59/$0.79 for the fastest inference at 315 TPS. Cloudflare offers a free tier with daily limits.

Is Llama 3.3 70B as good as GPT-4o?

On benchmarks, yes — both score ~72% on SWE-bench and ~88% on MMLU. In practice, GPT-4o has slight edges in instruction following. Llama 3.3 70B costs 86-96% less and is open-source.

What hardware do I need to run Llama 3.3 70B locally?

Full precision needs ~140GB VRAM (2x A100). Quantized to Q4_K_M, it runs on ~40GB (A6000 or Mac M4 Max). Most teams use API providers instead of self-hosting.

Should I upgrade from Llama 3.3 70B to Llama 4 Scout?

Only if speed or cost matters more than quality. Scout is faster (594 vs 315 TPS on Groq) and cheaper ($0.11 vs $0.59 input) but scores 4-5 points lower on coding benchmarks. Stay on 3.3 70B for quality-sensitive work.

Which Llama 3.3 70B provider is fastest?

Groq at 315.6 tokens per second — 6-12x faster than most competitors. SambaNova is second at 294 TPS. [Fireworks](https://tokenmix.ai/blog/fireworks-ai-review) has the lowest time-to-first-token at 0.6 seconds.

Is Llama 3.3 70B better than DeepSeek V4?

No. DeepSeek V4 scores higher on benchmarks (81% vs 72% SWE-bench) and is cheaper ($0.30/$0.50 vs $0.35/$0.35 at best). Llama's advantages: open weights for self-hosting, more provider options, no China data routing concerns.

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*Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: [Meta Llama](https://www.llama.com/models/llama-3/), [Artificial Analysis](https://artificialanalysis.ai/models/llama-3-3-instruct-70b), and [TokenMix.ai](https://tokenmix.ai)*