Gemini 3.1 Flash Lite vs Macaron V1 Tall vs CoBuddy
Gemini 3.1 Flash Lite comes out ahead, 72 to 55 and 46 on our weighted score, though CoBuddy is 12% cheaper per token.
- Our pick
GoogleGemini 3.1 Flash Lite
72/100- ECI—
- Price$0.25 / $1.50
- Context1.05M
Mindlab
Macaron V1 Tall
55/100- ECI—
- Price$0.247 / $1.43
- Context262K
BaiduCoBuddy
46/100- ECI—
- Price$0.28 / $1.13
- Context131K
Gemini 3.1 Flash Lite is our pick
Gemini 3.1 Flash Lite is the better all-round choice, scoring 72/100 against Macaron V1 Tall (55) and CoBuddy (46). It leads on inputs & features and context window. CoBuddy 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 priceCoBuddyCoBuddy $0.492 · Macaron V1 Tall $0.543 · Gemini 3.1 Flash Lite $0.563 per 1M tokens (3:1 blend)
- Longest contextGemini 3.1 Flash LiteGemini 3.1 Flash Lite 1,048,576 · Macaron V1 Tall 262,144 · CoBuddy 131,072 tokens
- Widest inputsGemini 3.1 Flash LiteGemini 3.1 Flash Lite: Text, Images, PDFs, Audio, Video · Macaron V1 Tall: Text, Images · CoBuddy: Text
- Self-hostingMacaron V1 TallPublishes downloadable weights (MIT)
| Measure | Weight | Gemini 3.1 Flash Lite | Macaron V1 Tall | CoBuddy |
|---|---|---|---|---|
| Price | 50% | 59 | 60 | 62 |
| Inputs & features | 30% | 100 | 60 | 35 |
| Context window | 20% | 61 | 37 | 25 |
| Overall | 100% | 72/100 | 55/100 | 46/100 |
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 3.1 Flash LiteGoogle | Macaron V1 TallMindlab | CoBuddyBaidu |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.247 (best) | $0.28 |
| Output | $1.50 | $1.43 | $1.13 (best) |
| Cached input | $0.025 | — | — |
| Blended (3:1) | $0.563 | $0.543 | $0.492 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Google API | Median of 1 providers | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 262,144 tokens | 131,072 tokens |
| Max output | 65,536 tokens (best) | 32,768 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yesminimal · low · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenMIT | Proprietary |
| API model ID | gemini-3.1-flash-lite | — | — |
| API providers | 26 (best) | 1 | 1 |
| Released | May 7, 2026 | Jul 21, 2026 | May 6, 2026 |
| 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 3.1 Flash Lite$5.50
- Macaron V1 Tall$5.33
- CoBuddy$5.06
Which should you choose?
Which is better: Gemini 3.1 Flash Lite, Macaron V1 Tall or CoBuddy?
Gemini 3.1 Flash Lite is the better all-round choice, scoring 72/100 against Macaron V1 Tall (55) and CoBuddy (46). It leads on inputs & features and context window. CoBuddy 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 3.1 Flash Lite, Macaron V1 Tall or CoBuddy?
CoBuddy is cheaper at $0.28 input / $1.13 output per million tokens (median across 1 API provider). Macaron V1 Tall costs $0.247 input / $1.43 output per million tokens (median across 1 API provider); Gemini 3.1 Flash Lite costs $0.25 input / $1.50 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.492 per million tokens for CoBuddy versus $0.543 for Macaron V1 Tall (1.1× as much) and $0.563 for Gemini 3.1 Flash Lite (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemini 3.1 Flash Lite has not been scored yet, Macaron V1 Tall has not been scored yet and CoBuddy has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 3.1 Flash Lite, Macaron V1 Tall and CoBuddy 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 3.1 Flash Lite has the largest context window at 1,048,576 tokens, against 262,144 for Macaron V1 Tall and 131,072 for CoBuddy. Maximum output per response: Gemini 3.1 Flash Lite up to 65,536, Macaron V1 Tall up to 32,768, CoBuddy up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Gemini 3.1 Flash Lite accepts text, images, PDFs, audio and video; Macaron V1 Tall accepts text and images; CoBuddy accepts text. Gemini 3.1 Flash Lite handles the widest range of inputs.
Are any of these open source?
Macaron V1 Tall publishes its weights (MIT) and can be self-hosted; Gemini 3.1 Flash Lite and CoBuddy is proprietary.
Which is newer?
Macaron V1 Tall is the newest, released Jul 21, 2026. Gemini 3.1 Flash Lite came out May 7, 2026; CoBuddy came out May 6, 2026. Knowledge cutoff: Gemini 3.1 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.
