Gemini 2.5 Flash-Lite vs Ministral 14B vs Rnj-1 Instruct
Gemini 2.5 Flash-Lite comes out ahead, 83 to 63 and 50 on our weighted score, though Rnj-1 Instruct is 14% cheaper per token.
- Our pick
GoogleGemini 2.5 Flash-Lite
83/100- ECI—
- Price$0.10 / $0.40
- Context1.05M
Mistral AIMinistral 14B
63/100- ECI—
- Price$0.175 / $0.175
- Context262K
Essential AI
Rnj-1 Instruct
50/100- ECI—
- Price$0.15 / $0.15
- Context33K
Gemini 2.5 Flash-Lite is our pick
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Ministral 14B (63) and Rnj-1 Instruct (50). It leads on inputs & features and context window. Rnj-1 Instruct wins on price. 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 InstructRnj-1 Instruct $0.15 · Gemini 2.5 Flash-Lite $0.175 · Ministral 14B $0.175 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · Ministral 14B 262,144 · Rnj-1 Instruct 32,768 tokens
- Widest inputsGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Ministral 14B: Text, Images · Rnj-1 Instruct: Text
- Self-hostingMinistral 14B and Rnj-1 InstructPublishes downloadable weights (Apache-2.0 and Apache 2.0)
| Measure | Weight | Gemini 2.5 Flash-Lite | Ministral 14B | Rnj-1 Instruct |
|---|---|---|---|---|
| Price | 50% | 82 | 82 | 85 |
| Inputs & features | 30% | 100 | 50 | 25 |
| Context window | 20% | 61 | 37 | 0 |
| Overall | 100% | 83/100 | 63/100 | 50/100 |
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 | Gemini 2.5 Flash-LiteGoogle | Ministral 14BMistral AI | Rnj-1 InstructEssential AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.10 (best) | $0.175 | $0.15 |
| Output | $0.40 | $0.175 | $0.15 (best) |
| Cached input | $0.01 | — | — |
| Blended (3:1) | $0.175 | $0.175 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Google API | Median of 2 providers | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 262,144 tokens | 32,768 tokens |
| Max output | 65,536 tokens | 262,144 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | OpenApache 2.0 |
| API model ID | gemini-2.5-flash-lite | — | — |
| API providers | 24 (best) | 2 | 1 |
| Released | Jun 17, 2025 | Dec 2, 2025 | Dec 4, 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.
- Gemini 2.5 Flash-Lite$1.80
- Ministral 14B$2.10
- Rnj-1 Instruct$1.80
Which should you choose?
Which is better: Gemini 2.5 Flash-Lite, Ministral 14B or Rnj-1 Instruct?
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 83/100 against Ministral 14B (63) and Rnj-1 Instruct (50). It leads on inputs & features and context window. Rnj-1 Instruct wins on price. 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, Gemini 2.5 Flash-Lite, Ministral 14B or Rnj-1 Instruct?
Rnj-1 Instruct is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). Gemini 2.5 Flash-Lite costs $0.10 input / $0.40 output per million tokens (official Google API price); Ministral 14B costs $0.175 input / $0.175 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Rnj-1 Instruct versus $0.175 for Gemini 2.5 Flash-Lite (1.2× as much) and $0.175 for Ministral 14B (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 2.5 Flash-Lite has not been scored yet, Ministral 14B has not been scored yet and Rnj-1 Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.5 Flash-Lite, Ministral 14B and Rnj-1 Instruct 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 14B and 32,768 for Rnj-1 Instruct. Maximum output per response: Gemini 2.5 Flash-Lite up to 65,536, Ministral 14B up to 262,144, Rnj-1 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Ministral 14B accepts text and images; Rnj-1 Instruct accepts text. Gemini 2.5 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Ministral 14B and Rnj-1 Instruct publishes its weights (Apache-2.0 and 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 14B 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.
