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Head-to-head comparison

Jamba Large 1.7 vs GPT-4o (2024-11-20)

Jamba Large 1.7 (AI21 Labs) and GPT-4o (2024-11-20) (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-4o (2024-11-20) holds the stronger ModelCap Index position (#108 vs #110); Jamba Large 1.7 is 1.3× cheaper per output token ($8.00/1M vs $10.00/1M); and Jamba Large 1.7 offers the longer context window (256K vs 128K tokens).

Which should you choose?

Choose Jamba Large 1.7 if…

  • API cost matters — $8.00/1M output tokens against $10.00/1M, about 1.3× cheaper.
  • your workload is prompt-heavy — input tokens cost $2.00/1M against $2.50/1M.
  • you need the longer context window — 256K tokens against 128K.
  • you want the more recently listed model — Jamba Large 1.7 was listed 8 August 2025, GPT-4o (2024-11-20) 20 November 2024.

Choose GPT-4o (2024-11-20) if…

  • you want the stronger overall ModelCap Index position — #108 against #110 (22.5 vs 22.5 points).

Jamba Large 1.7 vs GPT-4o (2024-11-20): specs, pricing and context

Specification comparison of Jamba Large 1.7 and GPT-4o (2024-11-20)
FieldJamba Large 1.7AI21 LabsGPT-4o (2024-11-20)OpenAI
ModelCap Index position#110#108
Index score22.522.5
EvidenceEstimatedEstimated
Input price / 1M tokens$2.00$2.50
Output price / 1M tokens$8.00$10.00
Context window256K tokens128K tokens
Max output tokens4K16K
API providers listed11
Weight accessGated accessAPI only
Input modalitiesTextText, Image, File
First listed8 August 202520 November 2024
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-4o (2024-11-20)

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-4o (2024-11-20): common questions

Is Jamba Large 1.7 better than GPT-4o (2024-11-20)?

GPT-4o (2024-11-20) ranks higher on the ModelCap Index as of 17 August 2026: #108 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-4o (2024-11-20)?

Jamba Large 1.7 is cheaper on output tokens: $8.00/1M against $10.00/1M. Input tokens are $2.00/1M for Jamba Large 1.7 and $2.50/1M for GPT-4o (2024-11-20). 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-4o (2024-11-20)?

Jamba Large 1.7 has the larger context window: 256K tokens against 128K.

Which is better for coding, Jamba Large 1.7 or GPT-4o (2024-11-20)?

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-4o (2024-11-20) open-weight models?

Jamba Large 1.7: Gated access. GPT-4o (2024-11-20): 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-4o (2024-11-20)?

ModelCap currently lists 1 API provider for Jamba Large 1.7 and 1 for GPT-4o (2024-11-20), 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-4o (2024-11-20) 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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