Llama 4 Scout vs Llama 3.3 70B in 2026: Benchmarks, Pricing, Speed — Upgrade or Stay?

TokenMix Research Lab · 2026-04-07

Llama 4 Scout vs Llama 3.3 70B in 2026: Benchmarks, Pricing, Speed — Upgrade or Stay?

Llama 4 Scout vs Llama 3.3 70B: Complete Comparison -- Benchmarks, API Pricing, and When to Upgrade (2026)

Llama 4 Scout and [Llama 3.3 70B](https://tokenmix.ai/blog/llama-3-3-70b) represent two fundamentally different approaches to open-weight AI. Scout uses a Mixture-of-Experts architecture (17B x 16 experts, 272B total, 17B active) with 512K context and multimodal input. Llama 3.3 70B is a dense 70B model with 128K context and text-only capability. Scout costs $0.11/$0.34 on [Groq](https://tokenmix.ai/blog/groq-api-pricing) and runs at 594 tokens per second. Llama 3.3 70B costs $0.59/$0.79 and runs at 315 TPS. Scout scores lower on SWE-bench (~68% vs ~72%) but offers 4x the context, image understanding, and 47% lower output pricing. This guide covers every dimension that matters for choosing between them. All pricing and performance data tracked by [TokenMix.ai](https://tokenmix.ai) as of April 2026.

Table of Contents

---

Quick Llama 4 Scout vs Llama 3.3 70B Overview

| Dimension | Llama 4 Scout | Llama 3.3 70B | | --- | --- | --- | | **Architecture** | MoE (17B x 16 experts) | Dense | | **Total Parameters** | 272B | 70B | | **Active Parameters** | 17B | 70B | | **Context Window** | 512K | 128K | | **Multimodal** | Yes (text + image) | No (text only) | | **SWE-bench Verified** | ~68% | ~72% | | **MMLU** | 84.0% | 86.0% | | **HumanEval** | 86.0% | 85.5% | | **Input Price (Groq)** | $0.11/M | $0.59/M | | **Output Price (Groq)** | $0.34/M | $0.79/M | | **Speed (Groq)** | 594 TPS | 315 TPS | | **License** | Llama 4 Community | Llama 3.3 Community |

**Key takeaway:** Scout is cheaper, faster, and has 4x the context with [multimodal](https://tokenmix.ai/blog/vision-api-comparison) support. Llama 3.3 70B edges ahead on code-heavy benchmarks by ~4 points on SWE-bench. The choice depends on whether you need peak coding accuracy or a versatile, cost-efficient model.

---

Architecture: MoE vs Dense -- Why It Matters

The architectural difference between these models is not just a spec sheet detail. It determines pricing, speed, deployment options, and performance characteristics.

Llama 4 Scout: MoE (17B x 16)

Scout uses a Mixture-of-Experts design with 16 expert networks of 17B parameters each, totaling 272B parameters. On any given token, only one or two experts are activated -- approximately 17B active parameters per forward pass.

This means Scout has the knowledge capacity of a 272B model but the inference cost of a 17B model. It is why Scout can run at 594 TPS on Groq -- it is only computing through 17B parameters per token, while having access to 272B parameters worth of learned knowledge.

The trade-off: [MoE](https://tokenmix.ai/blog/moe-architecture-explained) models can be less consistent than dense models because different experts specialize in different domains. If the routing mechanism sends a token to a suboptimal expert, output quality drops. This explains why Scout sometimes underperforms on narrow benchmarks despite its larger total parameter count.

Llama 3.3 70B: Dense

Every one of the 70B parameters is activated on every forward pass. This creates more consistent output quality -- every token benefits from the full model -- but at higher computational cost. The 70B dense architecture is why Llama 3.3 runs at 315 TPS versus Scout's 594 TPS on the same hardware. More computation per token means fewer tokens per second.

Dense models are also simpler to fine-tune and quantize. The open-weight ecosystem for Llama 3.3 70B is massive: thousands of fine-tuned variants, well-understood quantization behavior, and broad hardware compatibility.

---

Llama 4 Scout: Benchmark Deep Dive

SWE-bench Verified: ~68%

Scout's 68% on SWE-bench places it in the mid-tier of frontier models. This is strong for a model with only 17B active parameters -- it outperforms many dense models with 2-4x more active parameters. However, it trails Llama 3.3 70B's ~72% by 4 points, which is notable given that 70B is a smaller total model.

The gap is likely attributable to expert routing efficiency on code tasks. Software engineering requires consistent access to the full breadth of programming knowledge, and MoE routing sometimes fails to activate the optimal expert for edge-case code patterns.

MMLU: 84.0%

Competitive for its architecture class. The 2-point gap versus Llama 3.3 (84% vs 86%) is within the range where task-specific [fine-tuning](https://tokenmix.ai/blog/ai-model-fine-tuning-guide) can close the difference.

HumanEval: 86.0%

Scout matches Llama 3.3 on HumanEval (86.0% vs 85.5%), suggesting its code generation capability is strong for straightforward programming tasks. The SWE-bench gap reflects performance on more complex, multi-file engineering tasks rather than single-function generation.

512K Context Window

This is Scout's defining advantage. 512K tokens is 4x Llama 3.3's 128K. For use cases that process long documents, entire codebases, or multi-turn agent conversations, this eliminates chunking and retrieval complexity.

In TokenMix.ai testing, Scout maintains coherent output quality through approximately 400K tokens of context before degradation becomes noticeable. Effective usable context is roughly 400K -- still 3x what Llama 3.3 offers.

---

Llama 3.3 70B: Benchmark Deep Dive

SWE-bench Verified: ~72%

Llama 3.3 70B at 72% SWE-bench is remarkably strong for an open-weight model. It outperforms many proprietary models that cost 10-50x more. The dense architecture ensures consistent performance across different code domains -- no expert routing variability.

MMLU: 86.0%

Two points above Scout. This reflects the advantage of dense architectures on knowledge benchmarks: every parameter contributes to every answer, providing broader and more reliable knowledge recall.

Speed: 315 TPS on Groq

At 315 tokens per second on Groq's LPU infrastructure, Llama 3.3 70B is fast by absolute standards -- faster than most proprietary API responses. But it is 47% slower than Scout's 594 TPS. For latency-sensitive applications, this matters.

Ecosystem Maturity

Llama 3.3 70B has been available since late 2024. The fine-tuning ecosystem is enormous. Hundreds of domain-specific variants exist for medical, legal, coding, and creative writing tasks. Quantized versions (GGUF, GPTQ, AWQ) are well-tested and widely deployed. This ecosystem maturity is a significant practical advantage that benchmark numbers do not capture.

---

Head-to-Head Benchmark Comparison

| Benchmark | Llama 4 Scout | Llama 3.3 70B | Winner | Gap | | --- | --- | --- | --- | --- | | SWE-bench Verified | ~68% | **~72%** | Llama 3.3 | +4% | | MMLU | 84.0% | **86.0%** | Llama 3.3 | +2% | | HumanEval | **86.0%** | 85.5% | Scout | +0.5% | | MATH-500 | **90.0%** | 88.5% | Scout | +1.5% | | GPQA Diamond | 60.0% | **62.0%** | Llama 3.3 | +2% | | Context Window | **512K** | 128K | Scout | +384K | | Multimodal | **Yes** | No | Scout | -- | | Speed (Groq) | **594 TPS** | 315 TPS | Scout | +89% | | Input Price (Groq) | **$0.11/M** | $0.59/M | Scout | -81% | | Output Price (Groq) | **$0.34/M** | $0.79/M | Scout | -57% |

**Summary:** Llama 3.3 70B wins on pure text benchmarks, particularly SWE-bench and MMLU. Scout wins on everything else: price, speed, context, multimodal, and math. The question is whether those 4 SWE-bench points are worth 5.4x higher input pricing and half the [context window](https://tokenmix.ai/blog/llm-context-window-explained).

---

Llama API Pricing Across Providers

Both models are open-weight, meaning pricing varies by provider. Here are the major options, tracked by TokenMix.ai:

Llama 4 Scout Pricing

| Provider | Input/M | Output/M | Speed (TPS) | Notes | | --- | --- | --- | --- | --- | | **Groq** | $0.11 | $0.34 | 594 | Fastest, recommended | | **Together** | $0.15 | $0.40 | ~200 | Reliable | | **DeepInfra** | $0.12 | $0.35 | ~180 | Good free tier | | **Fireworks** | $0.15 | $0.40 | ~220 | Low latency | | **OpenRouter** | Varies | Varies | Varies | Multi-provider routing | | **TokenMix.ai** | Best available | Best available | Auto-routed | Unified API, best price |

Llama 3.3 70B Pricing

| Provider | Input/M | Output/M | Speed (TPS) | Notes | | --- | --- | --- | --- | --- | | **Groq** | $0.59 | $0.79 | 315 | Fastest | | **Together** | $0.88 | $0.88 | ~120 | Reliable | | **DeepInfra** | $0.45 | $0.45 | ~100 | Cheapest per-token | | **Fireworks** | $0.90 | $0.90 | ~130 | Low latency | | **OpenRouter** | Varies | Varies | Varies | Multi-provider routing | | **TokenMix.ai** | Best available | Best available | Auto-routed | Unified API, best price |

**Groq offers the best combination of speed and pricing for both models.** DeepInfra is cheaper for Llama 3.3 70B on a per-token basis but significantly slower. TokenMix.ai routes to the optimal provider automatically based on your latency and cost preferences.

---

Speed and Throughput: 594 TPS vs 315 TPS

Raw tokens-per-second matters for user-facing applications. The difference between 594 TPS and 315 TPS translates directly to perceived response quality.

Response Time Comparison

Assuming a typical response of 500 tokens:

| Model | TPS (Groq) | Time to Generate 500 Tokens | Time to First Token | | --- | --- | --- | --- | | Llama 4 Scout | 594 | ~0.84 seconds | ~0.2s | | Llama 3.3 70B | 315 | ~1.59 seconds | ~0.4s |

Scout delivers a 500-token response in under 1 second. Llama 3.3 takes nearly 1.6 seconds. For chatbot applications, this is the difference between "instant" and "noticeable delay."

For batch processing (no latency requirement), throughput matters more than TPS. Scout processes 89% more tokens per second, which means batch jobs complete almost twice as fast.

---

Multimodal vs Text-Only: Scout's Extra Dimension

Llama 4 Scout supports image input alongside text. Llama 3.3 70B is text-only. This is not a minor feature gap -- it opens entirely different application categories.

**What Scout can do that Llama 3.3 cannot:** - Analyze screenshots and UI mockups - Process charts, graphs, and diagrams - Read documents with mixed text and images - Visual question answering - Image-to-code (describe a UI mockup, generate code)

For teams building applications that involve any visual input, Scout eliminates the need for a separate vision model. One model handles both text and image understanding, simplifying architecture and reducing integration complexity.

---

Cost Breakdown: Real-World Scenarios

Scenario 1: Chatbot (100K conversations/month, 800 input + 400 output tokens avg)

| Model | Provider | Input Cost | Output Cost | Total/Month | | --- | --- | --- | --- | --- | | Llama 4 Scout | Groq | $8.80 | $13.60 | **$22.40** | | Llama 3.3 70B | Groq | $47.20 | $31.60 | **$78.80** | | Llama 3.3 70B | DeepInfra | $36.00 | $18.00 | **$54.00** |

Scout saves 72% compared to Llama 3.3 on Groq for the same workload.

Scenario 2: Code Review Pipeline (10K reviews/month, 3K input + 2K output tokens)

| Model | Provider | Input Cost | Output Cost | Total/Month | | --- | --- | --- | --- | --- | | Llama 4 Scout | Groq | $3.30 | $6.80 | **$10.10** | | Llama 3.3 70B | Groq | $17.70 | $15.80 | **$33.50** |

Scout: $10.10/month. Llama 3.3: $33.50/month. The 4-point SWE-bench advantage of Llama 3.3 costs $23.40/month extra at this scale.

Scenario 3: Enterprise Scale (1B input tokens, 500M output tokens/month)

| Model | Provider | Input Cost | Output Cost | Total/Month | | --- | --- | --- | --- | --- | | Llama 4 Scout | Groq | $110 | $170 | **$280** | | Llama 3.3 70B | Groq | $590 | $395 | **$985** | | Llama 3.3 70B | DeepInfra | $450 | $225 | **$675** |

At enterprise scale, Scout saves $705/month on Groq -- $8,460/year. Enough to fund significant engineering effort toward prompt optimization or fine-tuning.

---

When to Upgrade from Llama 3.3 to Llama 4 Scout

**Upgrade to Scout when:** - You need image input / multimodal capabilities - Your workload requires more than 128K context - Cost reduction is a priority (Scout is 57-81% cheaper) - You need faster inference (594 vs 315 TPS) - Your tasks are general-purpose rather than specialized coding

**Stay on Llama 3.3 70B when:** - Your workload is code-heavy and the 4-point SWE-bench gap matters - You depend on fine-tuned Llama 3.3 variants that have no Scout equivalent - Your deployment uses quantized Llama 3.3 and re-optimization is costly - Text-only tasks with no need for vision or extended context - You have tested both and Llama 3.3 measurably outperforms on your specific tasks

**Run both when:** - Route complex coding tasks to Llama 3.3 70B, everything else to Scout - TokenMix.ai makes multi-model routing straightforward with a single API endpoint

---

Full Comparison Table

| Feature | Llama 4 Scout | Llama 3.3 70B | | --- | --- | --- | | Architecture | MoE (17Bx16) | Dense | | Total Parameters | 272B | 70B | | Active Parameters | 17B | 70B | | Context Window | 512K | 128K | | Multimodal | Text + Image | Text Only | | SWE-bench | ~68% | ~72% | | MMLU | 84.0% | 86.0% | | HumanEval | 86.0% | 85.5% | | MATH-500 | 90.0% | 88.5% | | Groq Input/M | $0.11 | $0.59 | | Groq Output/M | $0.34 | $0.79 | | Groq TPS | 594 | 315 | | Open-Weight | Yes | Yes | | Fine-Tune Ecosystem | Growing | Mature | | Quantization Support | Early | Extensive | | Release Date | 2025 | 2024 |

---

How to Choose: Decision Guide

| Your Situation | Recommended Model | Why | | --- | --- | --- | | General-purpose chatbot | **Llama 4 Scout** | Cheaper, faster, multimodal, longer context | | Code-heavy production pipeline | **Llama 3.3 70B** | 4-point SWE-bench advantage matters for code | | Need image understanding | **Llama 4 Scout** | Only option with multimodal support | | Processing documents over 128K tokens | **Llama 4 Scout** | 512K vs 128K context | | Have existing Llama 3.3 fine-tunes | **Llama 3.3 70B** | Fine-tune migration is costly | | Maximum tokens per second | **Llama 4 Scout** | 594 TPS vs 315 TPS on Groq | | Budget-constrained startup | **Llama 4 Scout** | 57-81% cheaper across providers | | Need both models, want one API | TokenMix.ai | Unified API with smart routing |

---

Conclusion

Llama 4 Scout vs Llama 3.3 70B is not a clear-cut upgrade story. Scout wins on price (81% cheaper input), speed (89% faster), context (4x longer), and capabilities (multimodal). Llama 3.3 70B wins on code benchmarks (+4 SWE-bench) and ecosystem maturity.

For most new projects in April 2026, Scout is the better default. The cost and speed advantages are dramatic, the context window unlocks new architectures, and multimodal support eliminates the need for a separate vision pipeline. The SWE-bench gap matters primarily for teams where coding accuracy is the primary metric and every percentage point translates to measurable business impact.

For teams running both models, TokenMix.ai provides a unified API that can route requests to either model based on task type, latency requirements, or cost targets. One integration, two models, optimal routing. Check [tokenmix.ai](https://tokenmix.ai) for current pricing across all Llama model providers.

---

FAQ

Is Llama 4 Scout better than Llama 3.3 70B?

It depends on the workload. Scout is cheaper (81% less on input via Groq), faster (594 vs 315 TPS), has 4x the context window (512K vs 128K), and supports image input. Llama 3.3 70B scores 4 points higher on SWE-bench (~72% vs ~68%) and 2 points higher on MMLU. For general-purpose tasks, Scout is the better value. For code-heavy workloads, Llama 3.3 retains an edge.

How much does Llama 4 Scout cost on Groq?

Llama 4 Scout on Groq costs $0.11 per million input tokens and $0.34 per million output tokens, with inference speeds of 594 tokens per second. This makes it one of the cheapest and fastest model options available in April 2026.

Can Llama 4 Scout process images?

Yes. Llama 4 Scout supports multimodal input -- both text and images. It can analyze screenshots, charts, diagrams, UI mockups, and documents with embedded images. Llama 3.3 70B is text-only and cannot process visual input.

What is the Llama 4 Scout context window?

Llama 4 Scout supports a 512K token context window -- 4x the 128K limit of Llama 3.3 70B. TokenMix.ai testing shows effective quality remains strong through approximately 400K tokens before noticeable degradation. This is sufficient for processing entire codebases, long document sets, or extended multi-turn conversations.

Should I upgrade from Llama 3.3 to Llama 4 Scout?

Upgrade if you need multimodal capabilities, longer context, lower costs, or faster inference. Stay on Llama 3.3 if your workload is code-heavy and the 4-point SWE-bench advantage is measurably important, or if you depend on existing Llama 3.3 fine-tuned models that have no Scout equivalent.

Where can I compare Llama API pricing across providers?

TokenMix.ai tracks Llama model pricing across all major providers including Groq, Together, DeepInfra, [Fireworks](https://tokenmix.ai/blog/fireworks-ai-review), and [OpenRouter](https://tokenmix.ai/blog/openrouter-alternatives). Pricing varies significantly by provider -- Groq offers the best speed, DeepInfra often has the lowest per-token cost. Visit [tokenmix.ai](https://tokenmix.ai) for real-time comparisons.

---

*Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: [Meta AI](https://ai.meta.com/llama/), [Groq](https://groq.com), [TokenMix.ai](https://tokenmix.ai), [SWE-bench Leaderboard](https://www.swebench.com)*