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Head-to-head comparison

Jamba Large 1.7 vs GPT-3.5 Turbo Instruct

Jamba Large 1.7 (AI21 Labs) and GPT-3.5 Turbo Instruct (OpenAI) compared on the ModelCap Index, API price, context window, provider availability, weight access and every public benchmark board they share. Figures are the same ones shown on the live rankings; nothing here is a hidden score.

Snapshot as of 17 August 2026

As of 17 August 2026, GPT-3.5 Turbo Instruct holds the stronger ModelCap Index position (#107 vs #110); GPT-3.5 Turbo Instruct is 4× cheaper per output token ($2.00/1M vs $8.00/1M); and Jamba Large 1.7 offers the longer context window (256K vs 4K tokens).

Which should you choose?

Choose Jamba Large 1.7 if…

  • you need the longer context window — 256K tokens against 4K.
  • you want the more recently listed model — Jamba Large 1.7 was listed 8 August 2025, GPT-3.5 Turbo Instruct 28 September 2023.

Choose GPT-3.5 Turbo Instruct if…

  • you want the stronger overall ModelCap Index position — #107 against #110 (22.5 vs 22.5 points).
  • API cost matters — $2.00/1M output tokens against $8.00/1M, about 4× cheaper.
  • your workload is prompt-heavy — input tokens cost $1.50/1M against $2.00/1M.

Jamba Large 1.7 vs GPT-3.5 Turbo Instruct: specs, pricing and context

Specification comparison of Jamba Large 1.7 and GPT-3.5 Turbo Instruct
FieldJamba Large 1.7AI21 LabsGPT-3.5 Turbo InstructOpenAI
ModelCap Index position#110#107
Index score22.522.5
EvidenceEstimatedEstimated
Input price / 1M tokens$2.00$1.50
Output price / 1M tokens$8.00$2.00
Context window256K tokens4K tokens
Max output tokens4K4K
API providers listed11
Weight accessGated accessAPI only
Input modalitiesTextText
First listed8 August 202528 September 2023
PublisherAI21 LabsOpenAI

Evidence: global corpus prior · leave-one-anchor-out calibrated · publisher corpus prior · leave-one-anchor-out calibrated. Prices are the lowest listed API offer per million tokens observed on the OpenRouter catalogue.

Benchmark scores: Jamba Large 1.7 vs GPT-3.5 Turbo Instruct

Neither model has a published result on a public benchmark board ModelCap tracks yet.

Scores are the sources' own published figures for each model's best evaluated configuration; ModelCap never re-runs a benchmark.

Want a different pairing? Open the interactive comparison tool to swap either model for any current ranked language model.

Jamba Large 1.7 vs GPT-3.5 Turbo Instruct: common questions

Is Jamba Large 1.7 better than GPT-3.5 Turbo Instruct?

GPT-3.5 Turbo Instruct ranks higher on the ModelCap Index as of 17 August 2026: #107 against #110. That is a capability ranking built from public benchmark evidence with published uncertainty; whether it is "better" for you also depends on price, context and where you can run it.

Is Jamba Large 1.7 cheaper than GPT-3.5 Turbo Instruct?

GPT-3.5 Turbo Instruct is cheaper on output tokens: $2.00/1M against $8.00/1M. Input tokens are $2.00/1M for Jamba Large 1.7 and $1.50/1M for GPT-3.5 Turbo Instruct. Prices are the lowest listed API offer ModelCap observed, in USD per million tokens.

Which has the bigger context window, Jamba Large 1.7 or GPT-3.5 Turbo Instruct?

Jamba Large 1.7 has the larger context window: 256K tokens against 4K.

Which is better for coding, Jamba Large 1.7 or GPT-3.5 Turbo Instruct?

The two models do not share a coding benchmark board on ModelCap yet, so no head-to-head coding score is published; the ModelCap Index position is the closest overall signal.

Are Jamba Large 1.7 and GPT-3.5 Turbo Instruct open-weight models?

Jamba Large 1.7: Gated access. GPT-3.5 Turbo Instruct: API only. Open weights mean the checkpoint can be downloaded and self-hosted under its licence; API-only models are available solely through hosted endpoints.

Where can I run Jamba Large 1.7 and GPT-3.5 Turbo Instruct?

ModelCap currently lists 1 API provider for Jamba Large 1.7 and 1 for GPT-3.5 Turbo Instruct, from the OpenRouter catalogue snapshot the site serves; each model page lists the providers and their prices.

How current is this Jamba Large 1.7 vs GPT-3.5 Turbo Instruct comparison?

Every figure comes from the sealed ModelCap dataset published 17 August 2026; the page re-renders within a minute of each data refresh, and the ModelCap Index positions are the same ones shown on the live rankings.

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