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OpenAI's closed-weight GPT-4 Turbo brings a 128K-token context window and text+image input to complex, long-context tasks at interactive latency.
Key strengths. GPT-4 Turbo supports function/tool calling with parallel tool calls and a JSON response mode, features that were headline additions for this generation and remain available through the API. Independent benchmarking from Artificial Analysis now places its Intelligence Index and output-token throughput in the lower half of the current model field, and independent coding-benchmark results reflect a general-purpose model rather than a coding or reasoning specialist.
When to use it. Use GPT-4 Turbo for complex, long-context tasks — large-document analysis, multi-file code review, or multi-turn agentic workflows with tool calls — where interactive-latency responses and a 128K context window matter more than the newest reasoning capability. Its vision input also suits tasks that mix images with text instructions.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €9.07 / 27.22 | Online |
21 use cases classified by the curator. The note says why.
High safety benchmark (0.997) and tool-calling support suit customer-facing chatbot deployments requiring guardrail reliability.
128K context window supports full-document ingestion for accurate Q&A over long source material.
Native vision capability enables direct question-answering over image inputs.
0.997 on the Anthropic red-team safety set plus 128K context makes this a strong fit for compliance-sensitive policy Q&A workloads.
Large 128K context window enables synthesis across multiple long documents in a single pass.
128K context handles long transcripts/reports for summarisation without chunking.
Vision capability allows chart interpretation, but no dedicated chart-extraction benchmark is documented.
Vision input supports document image parsing, though not purpose-built or benchmarked as an OCR specialist.
Solid general instruction-following makes it a competent choice for routine business drafting tasks.
Native tool-calling capability suits structured function-calling application patterns.
128K context supports long-form drafting, though the family is positioned as a general instruct model rather than a creative specialist.
Text+image input supports screenshot interpretation tasks at interactive latency.
Weak coding benchmarks (21.5 coding index, 37.3 LiveCodeBench) rule this out for autonomous coding-agent workflows despite tool-calling support.
Low LiveCodeBench score (37.3) indicates weaker code-reasoning capability relevant to bug diagnosis.
Family posts only a 21.5 coding index and 37.3 LiveCodeBench score, materially behind code-tuned model releases.
No speech synthesis capability documented for this text+image instruct model.
Coding-heavy workloads lag newer code-tuned releases per the family's 21.5 coding index.
No documented audio input/output capability — model card lists only tool-calling and vision.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4-turbo",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at