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Comparison · 3 models · Updated Nov 2, 2026

o4-mini-deep-research vs Qwen3 VL 235B A22B Thinking vs Jamba Large

Qwen3 VL 235B A22B Thinking comes out ahead, 44 to 37 and 29 on our weighted score, and it is the cheaper option too.

  1. OpenAI

    o4-mini-deep-research

    Released Jun 26, 2025

    37/100
    • ECI—
    • Price$1.80 / $7.20
    • Context200K
    Full specs Remove o4-mini-deep-research
  2. Our pick

    Alibaba (Qwen)

    Qwen3 VL 235B A22B Thinking

    Released Sep 23, 2025

    44/100
    • ECI—
    • Price$0.50 / $3.95
    • Context131K
    Full specs Remove Qwen3 VL 235B A22B Thinking

  3. AI21 Labs

    Jamba Large

    Released Jul 3, 2025

    29/100
    • ECI—
    • Price$2.00 / $8.00
    • Context256K
    Full specs Remove Jamba Large
01 — Verdict

Qwen3 VL 235B A22B Thinking is our pick

Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 44/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. Jamba Large wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceQwen3 VL 235B A22B ThinkingQwen3 VL 235B A22B Thinking $1.36 · o4-mini-deep-research $3.15 · Jamba Large $3.50 per 1M tokens (3:1 blend)
  • Longest contextJamba LargeJamba Large 256,000 · o4-mini-deep-research 200,000 · Qwen3 VL 235B A22B Thinking 131,072 tokens
  • Widest inputso4-mini-deep-research and Qwen3 VL 235B A22B Thinkingo4-mini-deep-research: Text, Images · Qwen3 VL 235B A22B Thinking: Text, Images · Jamba Large: Text
  • Self-hostingQwen3 VL 235B A22B Thinking and Jamba LargePublishes downloadable weights
How the score is built
MeasureWeighto4-mini-deep-researchQwen3 VL 235B A22B ThinkingJamba Large
Price50%254223
Inputs & features30%606035
Context window20%322536
Overall100%37/10044/10029/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

o4-mini-deep-research vs Qwen3 VL 235B A22B Thinking vs Jamba Large specifications side by side
Specificationo4-mini-deep-researchOpenAIQwen3 VL 235B A22B ThinkingAlibaba (Qwen)Jamba LargeAI21 Labs
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$1.80$0.50 (best)$2.00
Output$7.20$3.95 (best)$8.00
Cached input———
Blended (3:1)$3.15$1.36 (best)$3.50
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 1 providersMedian of 5 providersOfficial AI21 Labs API
Limits
Context window200,000 tokens131,072 tokens256,000 tokens (best)
Max output100,000 tokens (best)32,768 tokens4,096 tokens
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesNo
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsProprietaryOpenOpen
API model ID——jamba-large
API providers15 (best)1
ReleasedJun 26, 2025Sep 23, 2025Jul 3, 2025
Knowledge cutoffMay 2024Mar 31, 2025Aug 22, 2024
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • o4-mini-deep-research$32.40
  • Qwen3 VL 235B A22B Thinking$12.90
  • Jamba Large$36.00
04 — Questions

Which should you choose?

Which is better: o4-mini-deep-research, Qwen3 VL 235B A22B Thinking or Jamba Large?

Qwen3 VL 235B A22B Thinking is the better all-round choice, scoring 44/100 against o4-mini-deep-research (37) and Jamba Large (29). It leads on price. Jamba Large wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, o4-mini-deep-research, Qwen3 VL 235B A22B Thinking or Jamba Large?

Qwen3 VL 235B A22B Thinking is cheaper at $0.50 input / $3.95 output per million tokens (median across 5 API providers). o4-mini-deep-research costs $1.80 input / $7.20 output per million tokens (median across 1 API provider); Jamba Large costs $2.00 input / $8.00 output per million tokens (official AI21 Labs API price). At a typical mix of three input tokens to one output token, that is $1.36 per million tokens for Qwen3 VL 235B A22B Thinking versus $3.15 for o4-mini-deep-research (2.3× as much) and $3.50 for Jamba Large (2.6× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. o4-mini-deep-research has not been scored yet, Qwen3 VL 235B A22B Thinking has not been scored yet and Jamba Large has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for o4-mini-deep-research, Qwen3 VL 235B A22B Thinking and Jamba Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Which has the bigger context window?

Jamba Large has the largest context window at 256,000 tokens, against 200,000 for o4-mini-deep-research and 131,072 for Qwen3 VL 235B A22B Thinking. Maximum output per response: o4-mini-deep-research up to 100,000, Qwen3 VL 235B A22B Thinking up to 32,768, Jamba Large up to 4,096 tokens.

Which can read images, PDFs, audio or video?

o4-mini-deep-research accepts text and images; Qwen3 VL 235B A22B Thinking accepts text and images; Jamba Large accepts text. o4-mini-deep-research handles the widest range of inputs.

Are any of these open source?

Qwen3 VL 235B A22B Thinking and Jamba Large publishes its weights and can be self-hosted; o4-mini-deep-research is proprietary.

Which is newer?

Qwen3 VL 235B A22B Thinking is the newest, released Sep 23, 2025. Jamba Large came out Jul 3, 2025; o4-mini-deep-research came out Jun 26, 2025. Knowledge cutoff: o4-mini-deep-research May 2024, Qwen3 VL 235B A22B Thinking Mar 31, 2025, Jamba Large Aug 22, 2024.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.

05 — Keep comparing

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