Llama-3.1-70B-Instruct vs Nova Pro vs GPT-4o mini
GPT-4o mini comes out ahead, 56 to 48 and 44 on our weighted score, and it is the cheaper option too.
MetaLlama-3.1-70B-Instruct
44/100- ECI125.9
- Price$0.72 / $0.72
- Context128K
AmazonNova Pro
48/100- ECI123.8
- Price$0.80 / $3.20
- Context300K
- Our pick
OpenAIGPT-4o mini
56/100- ECI126.6
- Price$0.15 / $0.60
- Context128K
GPT-4o mini is our pick
GPT-4o mini is the better all-round choice, scoring 56/100 against Nova Pro (48) and Llama-3.1-70B-Instruct (44). It leads on price. Nova Pro wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-4o miniCapabilities Index (ECI): GPT-4o mini 126.6 · Llama-3.1-70B-Instruct 125.9 · Nova Pro 123.8
- Lowest priceGPT-4o miniGPT-4o mini $0.263 · Llama-3.1-70B-Instruct $0.72 · Nova Pro $1.40 per 1M tokens (3:1 blend)
- Longest contextNova ProNova Pro 300,000 · Llama-3.1-70B-Instruct 128,000 · GPT-4o mini 128,000 tokens
- Widest inputsNova ProLlama-3.1-70B-Instruct: Text · Nova Pro: Text, Images, PDFs, Video · GPT-4o mini: Text, Images, PDFs
- Self-hostingLlama-3.1-70B-InstructPublishes downloadable weights
| Measure | Weight | Llama-3.1-70B-Instruct | Nova Pro | GPT-4o mini |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 48 | 45 | 49 |
| Price | 25% | 57 | 43 | 77 |
| Inputs & features | 15% | 25 | 70 | 70 |
| Context window | 10% | 24 | 39 | 24 |
| Overall | 100% | 44/100 | 48/100 | 56/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.1-70B-InstructMeta | Nova ProAmazon | GPT-4o miniOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 125.9 | 123.8 | 126.6 (best) |
| ECI rank | #136 of 148 | #137 of 148 | #135 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 44.2% (best) | — | 37.7% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 0.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | 3.6% | — | 6.9% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 8.3% |
| Price per million tokens | |||
| Input | $0.72 | $0.80 | $0.15 (best) |
| Output | $0.72 | $3.20 | $0.60 (best) |
| Cached input | — | $0.20 | $0.075 (best) |
| Blended (3:1) | $0.72 | $1.40 | $0.263 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Official Amazon Bedrock API | Official OpenAI API |
| Limits | |||
| Context window | 128,000 tokens | 300,000 tokens (best) | 128,000 tokens |
| Max output | 4,096 tokens | 10,000 tokens | 16,384 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | — | amazon.nova-pro-v1:0 | gpt-4o-mini |
| API providers | 5 | 3 | 21 (best) |
| Released | Jul 23, 2024 | Dec 3, 2024 | Jul 18, 2024 |
| Knowledge cutoff | Dec 2023 | Oct 2024 | Sep 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.1-70B-Instruct$8.64
- Nova Pro$14.40
- GPT-4o mini$2.70
Which should you choose?
Which is better: Llama-3.1-70B-Instruct, Nova Pro or GPT-4o mini?
GPT-4o mini is the better all-round choice, scoring 56/100 against Nova Pro (48) and Llama-3.1-70B-Instruct (44). It leads on price. Nova Pro wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama-3.1-70B-Instruct, Nova Pro or GPT-4o mini?
GPT-4o mini is cheaper at $0.15 input / $0.60 output per million tokens (official OpenAI API price). Llama-3.1-70B-Instruct costs $0.72 input / $0.72 output per million tokens (median across 5 API providers); Nova Pro costs $0.80 input / $3.20 output per million tokens (official Amazon Bedrock API price). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for GPT-4o mini versus $0.72 for Llama-3.1-70B-Instruct (2.7× as much) and $1.40 for Nova Pro (5.3× as much).
Which scores higher on benchmarks?
GPT-4o mini scores higher on the Capabilities Index (ECI): GPT-4o mini 126.6 (#135 of 148), Llama-3.1-70B-Instruct 125.9 (#136 of 148) and Nova Pro 123.8 (#137 of 148). The confidence ranges of the top two overlap (120.5–128.5 vs 121.0–128.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-70B-Instruct, Nova Pro and GPT-4o mini yet, so there is no like-for-like coding score. On overall capability, GPT-4o mini 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?
Nova Pro has the largest context window at 300,000 tokens, against 128,000 for Llama-3.1-70B-Instruct and 128,000 for GPT-4o mini. Maximum output per response: Llama-3.1-70B-Instruct up to 4,096, Nova Pro up to 10,000, GPT-4o mini up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-70B-Instruct accepts text; Nova Pro accepts text, images, PDFs and video; GPT-4o mini accepts text, images and PDFs. Nova Pro handles the widest range of inputs.
Are any of these open source?
Llama-3.1-70B-Instruct publishes its weights and can be self-hosted; Nova Pro and GPT-4o mini is proprietary.
Which is newer?
Nova Pro is the newest, released Dec 3, 2024. Llama-3.1-70B-Instruct came out Jul 23, 2024; GPT-4o mini came out Jul 18, 2024. Knowledge cutoff: Llama-3.1-70B-Instruct Dec 2023, Nova Pro Oct 2024, GPT-4o mini Sep 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.
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