Claude Haiku 3.5 vs Llama-3.3-70B-Instruct vs Mixtral 8x7B
Claude Haiku 3.5 comes out ahead, 49 to 41 and 31 on our weighted score.
- Our pick
AnthropicClaude Haiku 3.5
49/100- ECI127.2
- Price—
- Context200K
MetaLlama-3.3-70B-Instruct
41/100- ECI127.3
- Price$0.59 / $0.724
- Context128K
Mistral AIMixtral 8x7B
31/100- ECI118.5
- Price$0.70 / $0.70
- Context32K
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 · Llama-3.3-70B-Instruct: Text · Mixtral 8x7B: Text
- Self-hostingLlama-3.3-70B-Instruct and Mixtral 8x7BPublishes downloadable weights
| Measure | Weight | Claude Haiku 3.5 | Llama-3.3-70B-Instruct | Mixtral 8x7B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 49 | 49 | 38 |
| Inputs & features | 20% | 60 | 25 | 25 |
| Context window | 13% | 32 | 24 | 0 |
| Overall | 100% | 49/100 | 41/100 | 31/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Haiku 3.5Anthropic | Llama-3.3-70B-InstructMeta | Mixtral 8x7BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 127.2 | 127.3 (best) | 118.5 |
| ECI rank | #134 of 148 | #133 of 148 (best) | #142 of 148 |
| GPQA DiamondGraduate-level science questions | 38.1% | 47.4% (best) | 30.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 4.3% | 5.1% (best) | — |
| Price per million tokens | |||
| Input | — | $0.59 (best) | $0.70 |
| Output | — | $0.724 | $0.70 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.624 (best) | $0.70 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 21 providers | Official Mistral API |
| Limits | |||
| Context window | 200,000 tokens (best) | 128,000 tokens | 32,000 tokens |
| Max output | 8,192 tokens | 4,096 tokens | 32,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | llama-3.3-70b-instruct | open-mixtral-8x7b |
| API providers | — | 24 (best) | 1 |
| Released | Oct 22, 2024 | Dec 6, 2024 | Dec 11, 2023 |
| Knowledge cutoff | Jul 31, 2024 | Dec 2023 | Jan 2024 |
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—
- Llama-3.3-70B-Instruct$7.35
- Mixtral 8x7B$8.40
Which should you choose?
Which is better: Claude Haiku 3.5, Llama-3.3-70B-Instruct or Mixtral 8x7B?
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, Llama-3.3-70B-Instruct or Mixtral 8x7B?
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, Llama-3.3-70B-Instruct and Mixtral 8x7B 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, Llama-3.3-70B-Instruct up to 4,096, Mixtral 8x7B up to 32,000 tokens.
Which can read images, PDFs, audio or video?
Claude Haiku 3.5 accepts text, images and PDFs; Llama-3.3-70B-Instruct accepts text; Mixtral 8x7B accepts text. Claude Haiku 3.5 handles the widest range of inputs.
Are any of these open source?
Llama-3.3-70B-Instruct and Mixtral 8x7B 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, Llama-3.3-70B-Instruct Dec 2023, Mixtral 8x7B Jan 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.
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