GPT-4 Turbo: Long Context and More Reasonable Costs

GPT-4 Turbo (released November 2023, with iterations through April 2024) was the refresh that redefined OpenAI’s price/quality balance before GPT-4o. 128k token context, updated knowledge cutoff, 3x cheaper than original GPT-4. With GPT-4o in production, does Turbo still make sense? This article covers when GPT-4 Turbo remains the right choice and how to migrate without introducing regressions.

Key takeaways

• GPT-4 Turbo normalised long context (128k) and cut price 3x vs. original GPT-4; it is an intermediate generation already surpassed by GPT-4o on most dimensions.

• For new projects, GPT-4o is the default by price, speed and quality.

• For stable production that works well, migrating from Turbo to 4o is a days-long project, not urgent.

• The "lost in the middle" phenomenon affects all models with long contexts; for more than 100k tokens, Claude 3 Opus (200k) or Gemini 1.5 Pro (1M) may be better.

• Evaluation on your own golden set is the only reliable criterion before migrating in production.

GPT-4 Turbo versus GPT-4o

GPT-4o beats GPT-4 Turbo in price, speed and quality. For new projects, GPT-4o is the unambiguous default.

128k tokens: practical cases and real limitations

Where long context adds value:

• Long technical document analysis (~80k words without truncation).

• Codebase review with commit history.

• Long chat sessions with accumulated history.

• Extended transcription summarisation.

Limitations worth knowing:

• "Lost in the middle": models attend better to the start and end of context. Critical information in the middle is more likely to be missed, as the Stanford study on long contexts^[1] documented.

• Cost: 128k tokens at $10/1M input = $1.28 per query input only. With generation, $2–3 per typical query.

• Latency: processing 128k tokens takes 20–60 seconds.

For large but not massive context, consider Llama 3.1 405B or Gemini 1.5 Pro (1M context) if query volume justifies the switch.

Function calling and JSON mode

Turbo has solid function calling:

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }
}]

response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Weather in Madrid?"}],
    tools=tools
)

JSON mode guarantees valid JSON. Structured Outputs (GPT-4o+) goes further with strict JSON Schema and inline validation.

Pricing comparison in context

GPT-4o and Claude 3.5 Sonnet dominate the price/quality frontier. Turbo sits in the middle, outperformed by GPT-4o on both dimensions.

Migrating from Turbo to GPT-4o

If you have an app on Turbo and want to migrate:

• Model name change: gpt-4-turbo → gpt-4o in API calls.

• Benchmark on your golden set: quality should improve, but validate on real queries.

• Tokens: GPT-4o tokeniser slightly different; pricing lower.

• Rate limits: GPT-4o has different limits, check your tier.

• Behaviour: subtly different; some prompts may need adjustment.

For production apps, migrate in staging first. On a production app, one week of dev and testing.

When Turbo remains valid

• Contracts or compliance specifying a particular version.

• Stable production apps where "if it ain’t broken" applies.

• Deterministic testing that assumes Turbo’s specific behaviour.

• Third-party tools pinned to that version.

For new cases, the default is GPT-4o.

OpenAI’s release cycle

The pattern since 2023 has been consistent:

• GPT-4 (March 2023): frontier, expensive, 8k context.

• GPT-4 Turbo (November 2023): 128k, 3x cheaper.

• GPT-4o (May 2024): multimodal, 2x cheaper, faster.

• GPT-4o mini (July 2024): cheap GPT-3.5 replacement.

Every ~6 months, a significant refresh. Teams with religious loyalty to a specific model end up paying more for less. Evaluating each release without bias and migrating when the golden set confirms it is the rational strategy.

Conclusion

GPT-4 Turbo was an important update that normalised long context and cut the price to a third, but GPT-4o surpasses it on almost every dimension. For new projects, there is no technical reason to choose Turbo. For stable production that works, migrating to 4o is a days-long project without urgency.

If the goal is maximising context beyond 128k, Claude 3 Opus or Gemini 1.5 Pro are the alternatives to explore. The most useful lesson from Turbo is that frontier model price/quality improves regularly: teams that keep up with releases without dogmatism get better results at lower cost.

This article is also available in Spanish.

Sources:

1. OpenAI — New models and developer products announced at DevDay^[2]
2. Liu et al. (Stanford/Berkeley) — Lost in the Middle: How Language Models Use Long Contexts^[1]
3. Anthropic — Introducing the next generation of Claude (Claude 3 family)^[3]
4. Google — Introducing Gemini 1.5, Google’s next-generation AI model^[4]

Frequently asked questions

Is it still worth using GPT-4 Turbo now that GPT-4o exists?

Not for new projects. GPT-4o costs half as much ($5/$15 per million tokens versus $10/$30) and has lower first-token latency (~500 ms versus ~700 ms). It generates faster (~80 tokens/s versus ~30) and scores higher on MMLU (88.7 versus 86.4). Turbo remains valid only when a contract or compliance requirement pins that version, when a stable production app works well, in deterministic testing, or with third-party tools pinned to it.

How much does a query using the full 128k context cost, and how long does it take?

Input alone, 128k tokens at $10 per million comes to $1.28 per query; with generation, a typical query lands at $2-3. Processing those 128k tokens takes 20 to 60 seconds. On top of that, because of the "lost in the middle" effect, information placed in the centre of the context is more likely to be missed than content at the start or end. A long context does not guarantee everything is taken into account.

What does migrating an app from GPT-4 Turbo to GPT-4o involve?

Change the model name from gpt-4-turbo to gpt-4o in your API calls and validate quality on your golden set with real queries. Then check the rate limits for your tier (GPT-4o has different limits) and adjust any prompts that rely on Turbo's subtle behaviour. The tokeniser is slightly different and pricing is lower. For production apps, go through staging first; budget a week of development and testing.

Sources

1. Stanford study on long contexts
2. OpenAI — New models and developer products announced at DevDay
3. Anthropic — Introducing the next generation of Claude (Claude 3 family)
4. Google — Introducing Gemini 1.5, Google’s next-generation AI model