Rnj-1 Instruct vs Gemini 2.5 Flash-Lite vs Ministral 3 8B
Gemini 2.5 Flash-Lite comes out ahead, 83 to 68 and 50 on our weighted score, though Rnj-1 Instruct is 14% cheaper per token.
Essential AI
Rnj-1 Instruct
50/100- ECI—
- Price$0.15 / $0.15
- Context33K
- Our pick
GoogleGemini 2.5 Flash-Lite
83/100- ECI—
- Price$0.10 / $0.40
- Context1.05M
Mistral AIMinistral 3 8B
68/100- ECI—
- Price$0.15 / $0.15
- Context262K
Gemini 2.5 Flash-Lite is our pick
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Ministral 3 8B (68) and Rnj-1 Instruct (50). It leads on inputs & features and 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 priceRnj-1 Instruct and Ministral 3 8BRnj-1 Instruct $0.15 · Ministral 3 8B $0.15 · Gemini 2.5 Flash-Lite $0.175 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · Ministral 3 8B 262,144 · Rnj-1 Instruct 32,768 tokens
- Widest inputsGemini 2.5 Flash-LiteRnj-1 Instruct: Text · Gemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Ministral 3 8B: Text, Images
- Self-hostingRnj-1 Instruct and Ministral 3 8BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Rnj-1 Instruct | Gemini 2.5 Flash-Lite | Ministral 3 8B |
|---|---|---|---|---|
| Price | 50% | 85 | 82 | 85 |
| Inputs & features | 30% | 25 | 100 | 60 |
| Context window | 20% | 0 | 61 | 37 |
| Overall | 100% | 50/100 | 83/100 | 68/100 |
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 | Rnj-1 InstructEssential AI | Gemini 2.5 Flash-LiteGoogle | Ministral 3 8BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.10 (best) | $0.15 |
| Output | $0.15 (best) | $0.40 | $0.15 (best) |
| Cached input | — | $0.01 (best) | $0.015 |
| Blended (3:1) | $0.15 (best) | $0.175 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Google API | Official Mistral API |
| Limits | |||
| Context window | 32,768 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 32,768 tokens | 65,536 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenApache 2.0 | Proprietary | OpenApache 2.0 |
| API model ID | — | gemini-2.5-flash-lite | ministral-8b-2512 |
| API providers | 1 | 24 (best) | 11 |
| Released | Dec 4, 2025 | Jun 17, 2025 | Dec 2, 2025 |
| Knowledge cutoff | — | Jan 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Rnj-1 Instruct$1.80
- Gemini 2.5 Flash-Lite$1.80
- Ministral 3 8B$1.80
Which should you choose?
Which is better: Rnj-1 Instruct, Gemini 2.5 Flash-Lite or Ministral 3 8B?
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Ministral 3 8B (68) and Rnj-1 Instruct (50). It leads on inputs & features and 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, Rnj-1 Instruct, Gemini 2.5 Flash-Lite or Ministral 3 8B?
Rnj-1 Instruct is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). Ministral 3 8B costs $0.15 input / $0.15 output per million tokens (official Mistral API price); Gemini 2.5 Flash-Lite costs $0.10 input / $0.40 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Rnj-1 Instruct versus $0.15 for Ministral 3 8B (1× as much) and $0.175 for Gemini 2.5 Flash-Lite (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Rnj-1 Instruct has not been scored yet, Gemini 2.5 Flash-Lite has not been scored yet and Ministral 3 8B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Rnj-1 Instruct, Gemini 2.5 Flash-Lite and Ministral 3 8B 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?
Gemini 2.5 Flash-Lite has the largest context window at 1,048,576 tokens, against 262,144 for Ministral 3 8B and 32,768 for Rnj-1 Instruct. Maximum output per response: Rnj-1 Instruct up to 32,768, Gemini 2.5 Flash-Lite up to 65,536, Ministral 3 8B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Rnj-1 Instruct accepts text; Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Ministral 3 8B accepts text and images. Gemini 2.5 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Rnj-1 Instruct and Ministral 3 8B publishes its weights (Apache 2.0) and can be self-hosted; Gemini 2.5 Flash-Lite is proprietary.
Which is newer?
Rnj-1 Instruct is the newest, released Dec 4, 2025. Ministral 3 8B came out Dec 2, 2025; Gemini 2.5 Flash-Lite came out Jun 17, 2025. Knowledge cutoff: Gemini 2.5 Flash-Lite Jan 2025.
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.
