Benchmarking Open-Source vs. Closed-Source Models in 2026
By Promptster Team · 2026-04-21
Two years ago, the gap between open-source and closed-source AI models was enormous. Proprietary models from OpenAI and Anthropic dominated every benchmark, and open-weight alternatives felt like a generation behind. That narrative has fundamentally changed in 2026.
Models like Llama 4, Mistral Large, and DeepSeek V3 are competing head-to-head with GPT-5 and Claude on tasks that used to be exclusive territory for closed-source providers. We ran a systematic comparison across coding, reasoning, and creative writing to see exactly where things stand.
The Test Setup
We compared seven models across three task categories. Open-source and open-weight models were tested using their respective APIs and through hosting providers like Together AI, Groq, Cerebras, and Fireworks AI, while closed-source models came directly from OpenAI, Anthropic, and Google. Note that not all models were tested in every category.
Open-source / open-weight models tested: Llama 4 Scout (via Together AI), Mistral Large (open-weight, via Mistral API), DeepSeek V3 (via DeepSeek API)
Closed-source models tested: GPT-5 (OpenAI), Claude Sonnet 4.5 (Anthropic), Gemini 2.5 Pro (Google), GPT-4o (OpenAI)
Not all models were included in every benchmark category -- we focused each table on the models most relevant to that task type. Each task was run five times per model. We scored responses using Promptster's evaluation system, which rates across four dimensions: relevance, accuracy, completeness, and clarity.
Benchmark Results
Coding Tasks
We tested function generation, debugging, and code refactoring in Python and TypeScript.
| Model | Type | Avg Score | Avg Latency | Cost per Prompt |
|---|---|---|---|---|
| GPT-5 | Closed | 4.7/5 | 2.6s | $0.018 |
| Claude Sonnet 4.5 | Closed | 4.8/5 | 3.1s | $0.013 |
| DeepSeek V3 | Open | 4.6/5 | 3.8s | $0.003 |
| Llama 4 Scout | Open | 4.3/5 | 1.9s | $0.004 |
| Mistral Large | Open-weight | 4.4/5 | 2.7s | $0.006 |
DeepSeek V3 is the standout here. It scores within striking distance of Claude Sonnet 4.5 and GPT-5 on coding tasks at roughly one-fifth the cost. Llama 4 Scout trades some quality for impressive speed, making it viable for high-volume coding assistance where cost matters more than perfection.
Reasoning and Analysis
We tested logical deduction, multi-step math problems, and document analysis with complex instructions.
| Model | Type | Avg Score | Avg Latency | Cost per Prompt |
|---|---|---|---|---|
| Claude Sonnet 4.5 | Closed | 4.8/5 | 3.4s | $0.014 |
| GPT-5 | Closed | 4.7/5 | 2.8s | $0.019 |
| Gemini 2.5 Pro | Closed | 4.6/5 | 2.5s | $0.010 |
| DeepSeek V3 | Open | 4.4/5 | 4.2s | $0.004 |
| Llama 4 Scout | Open | 4.0/5 | 2.1s | $0.004 |
| Mistral Large | Open-weight | 4.2/5 | 3.0s | $0.006 |
Reasoning is where closed-source models still hold a meaningful lead. The gap between Claude Sonnet 4.5's 4.8 and DeepSeek's 4.4 may look small on paper, but in practice it showed up as missed logical steps and occasional errors on multi-hop reasoning chains. For safety-critical reasoning, the proprietary models remain the safer bet.
Creative Writing
We tested marketing copy, storytelling, and technical blog writing.
| Model | Type | Avg Score | Avg Latency | Cost per Prompt |
|---|---|---|---|---|
| Claude Sonnet 4.5 | Closed | 4.7/5 | 3.2s | $0.013 |
| GPT-5 | Closed | 4.6/5 | 2.5s | $0.017 |
| Mistral Large | Open-weight | 4.5/5 | 2.8s | $0.006 |
| Llama 4 Scout | Open | 4.3/5 | 1.8s | $0.004 |
| DeepSeek V3 | Open | 4.2/5 | 3.9s | $0.003 |
Creative writing is where the gap has narrowed most dramatically. Mistral Large produces prose that is genuinely difficult to distinguish from Claude Sonnet 4.5 or GPT-5 output. It handles tone, structure, and stylistic instructions with confidence.
The Real Advantages of Each
Why choose open-source
- Cost: 3-5x cheaper per prompt, which compounds fast at scale
- Privacy: Run locally or on your own infrastructure -- your data never leaves your network (more on this in our data privacy and local hosting guide)
- Customization: Fine-tune on your own data without vendor restrictions
- No vendor lock-in: Switch inference providers freely since the model weights are public
Why choose closed-source
- Quality ceiling: Still the best option for complex reasoning and high-stakes tasks
- Ease of use: One API key, no infrastructure to manage
- Support and reliability: Enterprise SLAs, dedicated support, consistent uptime
- Faster iteration: Frontier labs ship improvements frequently with zero effort on your end
How to Run This Comparison Yourself
The beauty of Promptster is that you don't have to take our word for any of this. You can replicate this entire benchmark with your own prompts in minutes.
- Open Promptster and select providers that serve open-source models -- Together AI, Groq, Cerebras, or Fireworks AI
- Add a closed-source provider like OpenAI or Anthropic alongside them
- Paste a prompt from your actual workflow
- Run the comparison and check the evaluation scores
You can also use the sandbox mode to run three free tests without even setting up API keys. Once you see the results, save them and iterate on your prompt to see how each model responds to changes.
The Bottom Line
Open-source models in 2026 are not a compromise -- they are a legitimate choice for most production workloads. The gap on coding and creative tasks is negligible for many use cases, and the cost savings are substantial. Reasoning remains the one area where closed-source holds a clear edge.
The smartest approach is not picking a side. It is testing both against your specific tasks and letting the data tell you which model earns its spot in your stack.
Start comparing open-source and closed-source models now -- your first three tests are free in sandbox mode.