Compare/Llama 3.3 Instruct 70B vs Mixtral 8x7B Instruct

Llama 3.3 Instruct 70BvsMixtral 8x7B Instruct

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Meta

Llama 3.3 Instruct 70B

Input
$0.66/M
Output
$0.72/M
Speed
84 tok/s
TTFT
1.61s
Mistral

Mixtral 8x7B Instruct

Input
$0.45/M
Output
$0.7/M
Speed
TTFT

Winner by Category

Cheaper
Mixtral 8x7B Instruct
Faster (tok/s)
Llama 3.3 Instruct 70B
Lower Latency
Llama 3.3 Instruct 70B
Benchmarks (2-0)
Llama 3.3 Instruct 70B

Pricing Comparison

MetricLlama 3.3 Instruct 70BMixtral 8x7B Instruct
Input ($/M tokens)$0.66$0.45
Output ($/M tokens)$0.72$0.7
Cost for 1M input + 100K output tokens:
Llama 3.3 Instruct 70B$0.73
Mixtral 8x7B Instruct$0.52

Speed Comparison

Output Speed (tokens/s) — higher is better
Llama 3.3 Instruct 70B
84 tok/s
Mixtral 8x7B Instruct
Time to First Token (seconds) — lower is better
Llama 3.3 Instruct 70B
1.61s
Mixtral 8x7B Instruct

Editorial Analysis

Verdict. Llama 3.3 Instruct 70B wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Mixtral 8x7B Instruct is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mixtral 8x7B Instruct makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Llama 3.3 Instruct 70B is strongest on Coding Index (11.9), Intelligence Index (9.3). Mixtral 8x7B Instruct leads on Intelligence Index (2.0).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. Meta and Mistral sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Mistral often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Llama 3.3 Instruct 70B costs $30.60 ($367/year); Mixtral 8x7B Instruct costs $24.00 ($288/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.3 Instruct 70B ≈ $4.74/run, Mixtral 8x7B Instruct ≈ $3.65/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.3 Instruct 70B ≈ $204/run, Mixtral 8x7B Instruct ≈ $160/run. Mixtral 8x7B Instruct becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
9.32.0
Coding Index
11.9
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Llama 3.3 Instruct 70B2 wins
0 winsMixtral 8x7B Instruct

Frequently Asked Questions

Which is cheaper, Llama 3.3 Instruct 70B or Mixtral 8x7B Instruct?

Mixtral 8x7B Instruct is cheaper overall. Its blended price (3:1 input/output ratio) is $0.51/M tokens vs $0.68/M for Llama 3.3 Instruct 70B.

Which model performs better on benchmarks?

Llama 3.3 Instruct 70B wins 2 out of 12 benchmarks compared to 0 for Mixtral 8x7B Instruct. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Llama 3.3 Instruct 70B generates tokens faster at 84 tok/s vs — tok/s. Llama 3.3 Instruct 70B also has lower time-to-first-token (1.61s vs —s).

When should I use Llama 3.3 Instruct 70B vs Mixtral 8x7B Instruct?

Choose based on your priorities: Mixtral 8x7B Instruct for lower cost, Llama 3.3 Instruct 70B for stronger benchmark performance, and Llama 3.3 Instruct 70B for faster generation. For latency-sensitive apps, check the TTFT comparison above.