DeepSeek V4 Pro vs Gemini Robotics-ER 1.6 Preview vs Jamba Large
Gemini Robotics-ER 1.6 Preview comes out ahead, 46 to 39 and 29 on our weighted score, and it is the cheaper option too.
DeepSeek
DeepSeek V4 Pro
39/100- ECI—
- Price$1.60 / $3.30
- Context1M
- Our pick
Google
Gemini Robotics-ER 1.6 Preview
46/100- ECI—
- Price$1.00 / $5.00
- Context131K
AI21 Labs
Jamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
Gemini Robotics-ER 1.6 Preview is our pick
Gemini Robotics-ER 1.6 Preview is the better all-round choice, scoring 46/100 against DeepSeek V4 Pro (39) and Jamba Large (29). It leads on inputs & features. DeepSeek V4 Pro 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 priceGemini Robotics-ER 1.6 PreviewGemini Robotics-ER 1.6 Preview $2.00 · DeepSeek V4 Pro $2.03 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 ProDeepSeek V4 Pro 1,000,000 · Jamba Large 256,000 · Gemini Robotics-ER 1.6 Preview 131,072 tokens
- Widest inputsGemini Robotics-ER 1.6 PreviewDeepSeek V4 Pro: Text · Gemini Robotics-ER 1.6 Preview: Text, Images, Audio, Video · Jamba Large: Text
- Self-hostingDeepSeek V4 Pro and Jamba LargePublishes downloadable weights
| Measure | Weight | DeepSeek V4 Pro | Gemini Robotics-ER 1.6 Preview | Jamba Large |
|---|---|---|---|---|
| Price | 50% | 34 | 34 | 23 |
| Inputs & features | 30% | 35 | 80 | 35 |
| Context window | 20% | 60 | 25 | 36 |
| Overall | 100% | 39/100 | 46/100 | 29/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.60 | $1.00 (best) | $2.00 |
| Output | $3.30 (best) | $5.00 | $8.00 |
| Cached input | — | — | — |
| Blended (3:1) | $2.03 | $2.00 (best) | $3.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 56 providers | Median of 1 providers | Official AI21 Labs API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 131,072 tokens | 256,000 tokens |
| Max output | 384,000 tokens (best) | 65,536 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | — | jamba-large |
| API providers | 58 (best) | 1 | 1 |
| Released | Apr 24, 2026 | Apr 14, 2026 | Jul 3, 2025 |
| Knowledge cutoff | May 2025 | Jan 2025 | Aug 22, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
DeepSeek V4 Pro$22.60
Gemini Robotics-ER 1.6 Preview$20.00
Jamba Large$36.00
Which should you choose?
Which is better: DeepSeek V4 Pro, Gemini Robotics-ER 1.6 Preview or Jamba Large?
Gemini Robotics-ER 1.6 Preview is the better all-round choice, scoring 46/100 against DeepSeek V4 Pro (39) and Jamba Large (29). It leads on inputs & features. DeepSeek V4 Pro 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, DeepSeek V4 Pro, Gemini Robotics-ER 1.6 Preview or Jamba Large?
Gemini Robotics-ER 1.6 Preview is cheaper at $1.00 input / $5.00 output per million tokens (median across 1 API provider). DeepSeek V4 Pro costs $1.60 input / $3.30 output per million tokens (median across 56 API providers); 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 $2.00 per million tokens for Gemini Robotics-ER 1.6 Preview versus $2.03 for DeepSeek V4 Pro (1× as much) and $3.50 for Jamba Large (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek V4 Pro has not been scored yet, Gemini Robotics-ER 1.6 Preview 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 DeepSeek V4 Pro, Gemini Robotics-ER 1.6 Preview 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?
DeepSeek V4 Pro has the largest context window at 1,000,000 tokens, against 256,000 for Jamba Large and 131,072 for Gemini Robotics-ER 1.6 Preview. Maximum output per response: DeepSeek V4 Pro up to 384,000, Gemini Robotics-ER 1.6 Preview up to 65,536, Jamba Large up to 4,096 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4 Pro accepts text; Gemini Robotics-ER 1.6 Preview accepts text, images, audio and video; Jamba Large accepts text. Gemini Robotics-ER 1.6 Preview handles the widest range of inputs.
Are any of these open source?
DeepSeek V4 Pro and Jamba Large publishes its weights and can be self-hosted; Gemini Robotics-ER 1.6 Preview is proprietary.
Which is newer?
DeepSeek V4 Pro is the newest, released Apr 24, 2026. Gemini Robotics-ER 1.6 Preview came out Apr 14, 2026; Jamba Large came out Jul 3, 2025. Knowledge cutoff: DeepSeek V4 Pro May 2025, Gemini Robotics-ER 1.6 Preview Jan 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.