ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GPT-4o vs Llama-3.1-70B-Instruct vs Mixtral 8x22B

Too close to call on our weighted score (Llama-3.1-70B-Instruct 44, GPT-4o 43, Mixtral 8x22B 33). The right pick depends on what you value most.

  1. OpenAI

    GPT-4o

    Released May 13, 2024

    43/100
    • ECI129.0
    • Price$2.50 / $10.00
    • Context128K
  2. Meta

    Llama-3.1-70B-Instruct

    Released Jul 23, 2024

    44/100
    • ECI125.9
    • Price$0.72 / $0.72
    • Context128K
  3. Mistral AI

    Mixtral 8x22B

    Released Apr 17, 2024

    33/100
    • ECI122.0
    • Price$2.00 / $6.00
    • Context64K
01 — Verdict

Too close to call

It is close. Our weighted score puts them within a point (Llama-3.1-70B-Instruct 44/100, GPT-4o 43/100, Mixtral 8x22B 33/100), so choose by what matters most for your work: GPT-4o for raw capability and Llama-3.1-70B-Instruct on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGPT-4oCapabilities Index (ECI): GPT-4o 129.0 · Llama-3.1-70B-Instruct 125.9 · Mixtral 8x22B 122.0
  • Lowest priceLlama-3.1-70B-InstructLlama-3.1-70B-Instruct $0.72 · Mixtral 8x22B $3.00 · GPT-4o $4.38 per 1M tokens (3:1 blend)
  • Longest contextGPT-4o and Llama-3.1-70B-InstructGPT-4o 128,000 · Llama-3.1-70B-Instruct 128,000 · Mixtral 8x22B 64,000 tokens
  • Widest inputsGPT-4oGPT-4o: Text, Images, PDFs · Llama-3.1-70B-Instruct: Text · Mixtral 8x22B: Text
  • Self-hostingLlama-3.1-70B-Instruct and Mixtral 8x22BPublishes downloadable weights
How the score is built
MeasureWeightGPT-4oLlama-3.1-70B-InstructMixtral 8x22B
CapabilityCapabilities Index (ECI)50%524843
Price25%195727
Inputs & features15%702525
Context window10%242412
Overall100%43/10044/10033/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

GPT-4o vs Llama-3.1-70B-Instruct vs Mixtral 8x22B specifications side by side
SpecificationGPT-4oOpenAILlama-3.1-70B-InstructMetaMixtral 8x22BMistral AI
Capability
Capabilities Index (ECI)129.0 (best)125.9122.0
ECI rank#129 of 148 (best)#136 of 148#139 of 148
GPQA DiamondGraduate-level science questions48.9% (best)44.2%34.1%
OTIS Mock AIME 2024–2025Competition mathematics6.3% (best)3.6%—
Price per million tokens
Input$2.50$0.72 (best)$2.00
Output$10.00$0.72 (best)$6.00
Cached input$1.25——
Blended (3:1)$4.38$0.72 (best)$3.00
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIMedian of 5 providersOfficial Mistral API
Limits
Context window128,000 tokens (best)128,000 tokens (best)64,000 tokens
Max output16,384 tokens4,096 tokens64,000 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsYesNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-4o—open-mixtral-8x22b
API providers19 (best)51
ReleasedMay 13, 2024Jul 23, 2024Apr 17, 2024
Knowledge cutoffSep 2023Dec 2023Apr 2024
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • GPT-4o$45.00
  • Llama-3.1-70B-Instruct$8.64
  • Mixtral 8x22B$32.00
04 — Questions

Which should you choose?

Which is better: GPT-4o, Llama-3.1-70B-Instruct or Mixtral 8x22B?

It is close. Our weighted score puts them within a point (Llama-3.1-70B-Instruct 44/100, GPT-4o 43/100, Mixtral 8x22B 33/100), so choose by what matters most for your work: GPT-4o for raw capability and Llama-3.1-70B-Instruct on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GPT-4o, Llama-3.1-70B-Instruct or Mixtral 8x22B?

Llama-3.1-70B-Instruct is cheaper at $0.72 input / $0.72 output per million tokens (median across 5 API providers). Mixtral 8x22B costs $2.00 input / $6.00 output per million tokens (official Mistral API price); GPT-4o costs $2.50 input / $10.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.72 per million tokens for Llama-3.1-70B-Instruct versus $3.00 for Mixtral 8x22B (4.2× as much) and $4.38 for GPT-4o (6.1× as much).

Which scores higher on benchmarks?

GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#129 of 148), Llama-3.1-70B-Instruct 125.9 (#136 of 148) and Mixtral 8x22B 122.0 (#139 of 148). The confidence ranges of the top two overlap (124.2–131.5 vs 121.0–128.1), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4o 48.9%, Llama-3.1-70B-Instruct 44.2%, Mixtral 8x22B 34.1%.

Which is better for coding?

There are no published SWE-bench Verified results for GPT-4o, Llama-3.1-70B-Instruct and Mixtral 8x22B yet, so there is no like-for-like coding score. On overall capability, GPT-4o leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

Which has the bigger context window?

GPT-4o and Llama-3.1-70B-Instruct have the largest context windows (128,000 and 128,000 tokens), against 64,000 for Mixtral 8x22B. Maximum output per response: GPT-4o up to 16,384, Llama-3.1-70B-Instruct up to 4,096, Mixtral 8x22B up to 64,000 tokens.

Which can read images, PDFs, audio or video?

GPT-4o accepts text, images and PDFs; Llama-3.1-70B-Instruct accepts text; Mixtral 8x22B accepts text. GPT-4o handles the widest range of inputs.

Are any of these open source?

Llama-3.1-70B-Instruct and Mixtral 8x22B publishes its weights and can be self-hosted; GPT-4o is proprietary.

Which is newer?

Llama-3.1-70B-Instruct is the newest, released Jul 23, 2024. GPT-4o came out May 13, 2024; Mixtral 8x22B came out Apr 17, 2024. Knowledge cutoff: GPT-4o Sep 2023, Llama-3.1-70B-Instruct Dec 2023, Mixtral 8x22B Apr 2024.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.