The best language models for digital products

LLM Benchmarks | May 2024

Based on real benchmark data from our own software products, we evaluated the performance of different LLM models in addressing specific challenges. We examined specific categories such as document processing, CRM integration, external integration, marketing support, and code generation.  

Highlights:

LLM Benchmarks | May 2024

Our benchmarks evaluate the models in terms of their suitability for digital product development. The higher the score, the better.

☁️ - Cloud models with proprietary license
✅ - Open source models that can be run locally without restrictions
🦙 - Local models with Llama2 license

A more detailed explanation of the respective categories can be found below the table.

modelcodecrmdocsintegratemarketingreasonfinal 🏆CostSpeed
GPT-4o ☁️8595100908275881.24 €1.49 rps
GPT-4 Turbo v5/2024-04-09 ☁️809998938845842.51 €0.83 rps
GPT-4 v1/0314 ☁️808898528850767.19 €1.26 rps
GPT-4 Turbo v4/0125-preview ☁️6097100717545752.51 €0.82 rps
GPT-4 v2/0613 ☁️808395528850747.19 €2.07 rps
Claude 3 Opus ☁️6488100537659734.83 €0.41 rps
GPT-4 Turbo v3/1106-preview ☁️607598528862722.52 €0.68 rps
Gemini Pro 1.5 0514 ☁️6796751002562712.06 €0.91 rps
Gemini Pro 1.5 0409 ☁️629796637528701.89 €0.58 rps
GPT-3.5 v2/0613 ☁️627973758148700.35 €1.39 rps
GPT-3.5 v3/1106 ☁️626871637859670.24 €2.29 rps
Gemini 1.5 Flash 0514 ☁️3297100567241660.10 €1.76 rps
GPT-3.5 v4/0125 ☁️588571607847660.13 €1.41 rps
Gemini Pro 1.0 ☁️558683608826660.10 €1.35 rps
Cohere Command R+ ☁️587776497059650.85 €1.88 rps
Qwen1.5 32B Chat f16 ⚠️648782567815641.02 €1.61 rps
GPT-3.5-instruct 0914 ☁️449069608832640.36 €2.12 rps
Mistral 7B OpenChat-3.5 v3 0106 f16 ✅568667528826620.37 €2.99 rps
Meta Llama 3 8B Instruct f16🦙746068498042620.35 €3.16 rps
GPT-3.5 v1/0301 ☁️497569678224610.36 €3.93 rps
Starling 7B-alpha f16 ⚠️516667528836600.61 €1.80 rps
Mistral 7B OpenChat-3.5 v1 f16 ✅467272498831600.51 €2.14 rps
Claude 3 Haiku ☁️596964557533590.08 €0.53 rps
Mixtral 8x22B API (Instruct) ☁️47626294757580.18 €3.01 rps
Mistral 7B OpenChat-3.5 v2 1210 f16 ✅517472417531570.36 €3.05 rps
Claude 3 Sonnet ☁️674174527830570.97 €0.85 rps
Mistral Large v1/2402 ☁️334970758425562.19 €2.04 rps
Anthropic Claude Instant v1.2 ☁️517565596514552.15 €1.47 rps
Anthropic Claude v2.0 ☁️575255458435552.24 €0.40 rps
Cohere Command R ☁️396357558426540.13 €2.47 rps
Qwen1.5 7B Chat f16 ⚠️518160346036540.30 €3.62 rps
Anthropic Claude v2.1 ☁️365859607533532.31 €0.35 rps
Qwen1.5 14B Chat f16 ⚠️445851498417510.38 €2.90 rps
Meta Llama 3 70B Instruct b8🦙467253298218507.32 €0.22 rps
Mistral 7B OpenOrca f16 ☁️425776217826500.43 €2.55 rps
Mistral 7B Instruct v0.1 f16 ☁️317069446221500.79 €1.39 rps
Llama2 13B Vicuna-1.5 f16🦙363753398238481.02 €1.07 rps
Llama2 13B Hermes f16🦙382330616043421.03 €1.06 rps
Llama2 13B Hermes b8🦙322429616043424.94 €0.22 rps
Mistral Small v1/2312 (Mixtral) ☁️10586551568410.19 €2.17 rps
Mistral Small v2/2402 ☁️27353682568410.19 €3.14 rps
IBM Granite 34B Code Instruct f16 ☁️52493044575401.12 €1.46 rps
Llama2 13B Puffin f16🦙371238485641394.89 €0.22 rps
Mistral Medium v1/2312 ☁️363027596212380.83 €0.35 rps
Llama2 13B Puffin b8🦙37937465639378.65 €0.13 rps
Mistral Tiny v1/2312 (7B Instruct v0.2) ☁️13395740598360.05 €2.30 rps
Llama2 13B chat f16🦙15381745758330.76 €1.43 rps
Llama2 13B chat b8🦙15381545756323.35 €0.33 rps
Mistral 7B Zephyr-β f16 ✅28344644294310.51 €2.14 rps
Llama2 7B chat f16🦙203320425020310.59 €1.86 rps
Mistral 7B Notus-v1 f16 ⚠️16432541484300.80 €1.37 rps
Orca 2 13B f16 ⚠️152232226719290.99 €1.11 rps
Microsoft Phi 3 Mini 4K Instruct f16 ⚠️36242617508270.95 €1.15 rps
Mistral 7B Instruct v0.2 f16 ☁️7215013588261.00 €1.10 rps
Mistral 7B f16 ☁️0442425212250.93 €1.17 rps
Orca 2 7B f16 ⚠️1302418524190.81 €1.34 rps
Llama2 7B f16🦙0218328291.01 €1.08 rps

The benchmark categories in detail

Here's exactly what we're looking at with the different categories of LLM Leaderboards

How well can the model work with large documents and knowledge bases?

How well does the model support work with product catalogs and marketplaces?

Can the model easily interact with external APIs, services and plugins?

How well can the model support marketing activities, e.g. brainstorming, idea generation and text generation?

How well can the model reason and draw conclusions in a given context?

Can the model generate code and help with programming?

The estimated cost of running the workload. For cloud-based models, we calculate the cost according to the pricing. For on-premises models, we estimate the cost based on GPU requirements for each model, GPU rental cost, model speed, and operational overhead.

The "Speed" column indicates the estimated speed of the model in requests per second (without batching). The higher the speed, the better.


Deeper insights

Google Gemini 1.5 - Pro and Flash

The recent Google IO announcement was all about AI and Gemini Pro. Although Google managed to forget about Gemini Ultra (it was supposed to come out early this year), they did update a few models.

GEMINI PRO 1.5 0514: UPDATED CAPABILITIES

Gemini Pro 1.5 0514 trades off some document comprehension capabilities in favour of better reasoning. It comes out as a slightly better version than the previous Gemini Pro version.

In our experience it was also a bit buggy at this point. We’ve encountered a bunch of server errors and even managed to get “HARM_CATEGORY_DANGEROUS_CONTENT” flag on one of the business benchmarks.

EXCELLENT "INTEGRATE" SKILLS

Gemini Pro 1.5 scored a perfect "Integrate" score, where we measure LLM's ability to reliably follow instructions and work with external systems, plugins and data formats.

GEMINI 1.5 FLASH AS AN INTERESTING ALTERNATIVE TO GPT-3.5

Gemini 1.5 Flash is an interesting new addition to the family. It works well in document tasks and has decent reasoning capability. Combine that with a very low price, and you get a good alternative to GPT-3.5.

Just note Google Gemini's peculiar pricing model - they don't charge text by tokens, but by billable symbols(Unicode code points minus spaces). We have seen several developers and even SaaS systems make mistakes when estimating costs.

GPT-4o - Clearly in the lead, but with a caveat

GPT-4o looks perfect at first glance. It is faster and cheaper than GPT-4 turbo. It also has 128K context, scores higher, has native multi-modality and understands languages better.

Under the hood it has a new tokeniser with a bigger dictionary. It leads to reduced token counts.

Overall the model scores didn’t jump much higher, because we are already operating at the limit of our LLM Benchmark. There is just one gotcha. Our “Reason” category (ability of models to handle complex logical and reasoning tasks) was made inherently difficult. GPT-4o managed to increase score from 62 (GPT-4 Turbo v3/1106-preview) to 75.

What is the caveat?

You see, OpenAI seems to operate in cycles. They switch between: “let’s make a better model” and “let’s make a cheaper model without sacrificing quality too much”.

While the LLM Benchmarks don’t catch it, it feels like the GPT-4o model belongs to the cost reduction models. It works amazingly well on small prompts, however other benchmarks demonstrate that it is not as good in dealing with larger contexts, like the other GPT-4 models. It also feels lacking in reasoning, even though current benchmarks are not capable of catching this regression.

Hint

For the time being, GPT-4 Turbo v5/2024-04-09 is our recommended go-to model.

Qwen 1.5 Chat

HIGHLY DEMANDED BENCHMARK

Due to the high demand and good ratings in the LMSYS Arena, we decided to benchmark some flavours of Alibaba Cloud's Qwen Chat model.

EVALUATION OF QWEN 1.5 32B CHAT

Qwen 1.5 32B Chat is quite good. It is within the range of GPT-3.5 models and Gemini Pro 1.0. It comes with a non-standard license, though.

We have also tested Qwen 1.5 7B and 14B - they are quite good for their relative size. Nothing peculiar, just a decent performance.

 

The license of Qwen1.5 Chat is a Chinese equivalent of Llama 3: you can use it freely for commercial purposes, if you have less than 100M MAU. This might make model adoption tricky in the USA and EU.

IBM Granite 34B Code Instruct

That is the only peculiar thing about their models, though. While the previous versions of IBM Granite models were available only within the IBM Cloud, the model under test was published directly to Hugging Face.

However, that is the only special thing about their models. While the previous versions of the IBM Granite models were only available within the IBM Cloud, the tested model was published directly on Hugging Face.

Long story short, IBM Granite 34B Code Instruct has a decent code capability (for a 7B model) and bad results in pretty much everything else. If you need a local coding model at a fraction of compute cost, just pick Llama3 or one of its derivatives.


LLM Benchmarks Archive

Interested in the benchmarks of the past months? You can find all the links on our LLM Benchmarks overview page!

Learn more

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