ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Claude Haiku 3.5 vs Mixtral 8x7B vs Llama-3.3-70B-Instruct

Claude Haiku 3.5 comes out ahead, 49 to 41 and 31 on our weighted score.

  1. Our pick

    Anthropic

    Claude Haiku 3.5

    Released Oct 22, 2024

    49/100
    • ECI127.2
    • Price—
    • Context200K
    Full specs Remove Claude Haiku 3.5

  2. Mistral AI

    Mixtral 8x7B

    Released Dec 11, 2023

    31/100
    • ECI118.5
    • Price$0.70 / $0.70
    • Context32K
    Full specs Remove Mixtral 8x7B

  3. Meta

    Llama-3.3-70B-Instruct

    Released Dec 6, 2024

    41/100
    • ECI127.3
    • Price$0.59 / $0.724
    • Context128K
    Full specs Remove Llama-3.3-70B-Instruct
01 — Verdict

Claude Haiku 3.5 is our pick

Claude Haiku 3.5 is the better all-round choice, scoring 49/100 against Llama-3.3-70B-Instruct (41) and Mixtral 8x7B (31). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.

  • CapabilityLlama-3.3-70B-InstructCapabilities Index (ECI): Llama-3.3-70B-Instruct 127.3 · Claude Haiku 3.5 127.2 · Mixtral 8x7B 118.5
  • Lowest priceLlama-3.3-70B-InstructLlama-3.3-70B-Instruct $0.624 · Mixtral 8x7B $0.70 per 1M tokens (3:1 blend) · Claude Haiku 3.5 unpriced
  • Longest contextClaude Haiku 3.5Claude Haiku 3.5 200,000 · Llama-3.3-70B-Instruct 128,000 · Mixtral 8x7B 32,000 tokens
  • Widest inputsClaude Haiku 3.5Claude Haiku 3.5: Text, Images, PDFs · Mixtral 8x7B: Text · Llama-3.3-70B-Instruct: Text
  • Self-hostingMixtral 8x7B and Llama-3.3-70B-InstructPublishes downloadable weights
How the score is built
MeasureWeightClaude Haiku 3.5Mixtral 8x7BLlama-3.3-70B-Instruct
CapabilityCapabilities Index (ECI)67%493849
Inputs & features20%602525
Context window13%32024
Overall100%49/10031/10041/100

Left out because at least one model lacks the data: price. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

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

Claude Haiku 3.5 vs Mixtral 8x7B vs Llama-3.3-70B-Instruct specifications side by side
SpecificationClaude Haiku 3.5AnthropicMixtral 8x7BMistral AILlama-3.3-70B-InstructMeta
Capability
Capabilities Index (ECI)127.2118.5127.3 (best)
ECI rank#134 of 148#142 of 148#133 of 148 (best)
GPQA DiamondGraduate-level science questions38.1%30.6%47.4% (best)
OTIS Mock AIME 2024–2025Competition mathematics4.3%—5.1% (best)
Price per million tokens
Input—$0.70$0.59 (best)
Output—$0.70 (best)$0.724
Cached input———
Blended (3:1)—$0.70$0.624 (best)
Long-context rate—Same rateSame rate
Price source—Official Mistral APIMedian of 21 providers
Limits
Context window200,000 tokens (best)32,000 tokens128,000 tokens
Max output8,192 tokens32,000 tokens (best)4,096 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsYesNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsProprietaryOpenOpen
API model ID—open-mixtral-8x7bllama-3.3-70b-instruct
API providers—124 (best)
ReleasedOct 22, 2024Dec 11, 2023Dec 6, 2024
Knowledge cutoffJul 31, 2024Jan 2024Dec 2023
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.

  • Claude Haiku 3.5—
  • Mixtral 8x7B$8.40
  • Llama-3.3-70B-Instruct$7.35
04 — Questions

Which should you choose?

Which is better: Claude Haiku 3.5, Mixtral 8x7B or Llama-3.3-70B-Instruct?

Claude Haiku 3.5 is the better all-round choice, scoring 49/100 against Llama-3.3-70B-Instruct (41) and Mixtral 8x7B (31). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.

Which is cheaper, Claude Haiku 3.5, Mixtral 8x7B or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper at $0.59 input / $0.724 output per million tokens (median across 21 API providers; free on Llama). Mixtral 8x7B costs $0.70 input / $0.70 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.624 per million tokens for Llama-3.3-70B-Instruct versus $0.70 for Mixtral 8x7B (1.1× as much). Claude Haiku 3.5 has no published per-token price.

Which scores higher on benchmarks?

Llama-3.3-70B-Instruct scores higher on the Capabilities Index (ECI): Llama-3.3-70B-Instruct 127.3 (#133 of 148), Claude Haiku 3.5 127.2 (#134 of 148) and Mixtral 8x7B 118.5 (#142 of 148). The confidence ranges of the top two overlap (122.5–129.5 vs 120.7–129.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama-3.3-70B-Instruct 47.4%, Claude Haiku 3.5 38.1%, Mixtral 8x7B 30.6%.

Which is better for coding?

There are no published SWE-bench Verified results for Claude Haiku 3.5, Mixtral 8x7B and Llama-3.3-70B-Instruct yet, so there is no like-for-like coding score. On overall capability, Llama-3.3-70B-Instruct 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?

Claude Haiku 3.5 has the largest context window at 200,000 tokens, against 128,000 for Llama-3.3-70B-Instruct and 32,000 for Mixtral 8x7B. Maximum output per response: Claude Haiku 3.5 up to 8,192, Mixtral 8x7B up to 32,000, Llama-3.3-70B-Instruct up to 4,096 tokens.

Which can read images, PDFs, audio or video?

Claude Haiku 3.5 accepts text, images and PDFs; Mixtral 8x7B accepts text; Llama-3.3-70B-Instruct accepts text. Claude Haiku 3.5 handles the widest range of inputs.

Are any of these open source?

Mixtral 8x7B and Llama-3.3-70B-Instruct publishes its weights and can be self-hosted; Claude Haiku 3.5 is proprietary.

Which is newer?

Llama-3.3-70B-Instruct is the newest, released Dec 6, 2024. Claude Haiku 3.5 came out Oct 22, 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: Claude Haiku 3.5 Jul 31, 2024, Mixtral 8x7B Jan 2024, Llama-3.3-70B-Instruct Dec 2023.

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.

05 — Keep comparing

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