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.
OpenAI
GPT-4o
43/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
Meta
Llama-3.1-70B-Instruct
44/100- ECI125.9
- Price$0.72 / $0.72
- Context128K
Mistral AI
Mixtral 8x22B
33/100- ECI122.0
- Price$2.00 / $6.00
- Context64K
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
| Measure | Weight | GPT-4o | Llama-3.1-70B-Instruct | Mixtral 8x22B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 52 | 48 | 43 |
| Price | 25% | 19 | 57 | 27 |
| Inputs & features | 15% | 70 | 25 | 25 |
| Context window | 10% | 24 | 24 | 12 |
| Overall | 100% | 43/100 | 44/100 | 33/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.0 (best) | 125.9 | 122.0 |
| ECI rank | #129 of 148 (best) | #136 of 148 | #139 of 148 |
| GPQA DiamondGraduate-level science questions | 48.9% (best) | 44.2% | 34.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 6.3% (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 rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 5 providers | Official Mistral API |
| Limits | |||
| Context window | 128,000 tokens (best) | 128,000 tokens (best) | 64,000 tokens |
| Max output | 16,384 tokens | 4,096 tokens | 64,000 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 | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-4o | — | open-mixtral-8x22b |
| API providers | 19 (best) | 5 | 1 |
| Released | May 13, 2024 | Jul 23, 2024 | Apr 17, 2024 |
| Knowledge cutoff | Sep 2023 | Dec 2023 | Apr 2024 |
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
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.