Mixtral 8x22B Instruct vs Saba vs Gemma 2 27B
Saba comes out ahead, 46 to 28 and 26 on our weighted score, and it is the cheaper option too.
Mistral AIMixtral 8x22B Instruct
26/100- ECI122.0
- Price$2.00 / $6.00
- Context66K
- Our pickMistral AI
Saba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
GoogleGemma 2 27B
28/100- ECI122.1
- Price$0.65 / $0.65
- Context8K
Saba is our pick
Saba is the better all-round choice, scoring 46/100 against Gemma 2 27B (28) and Mixtral 8x22B Instruct (26). It leads on price. Mixtral 8x22B Instruct 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 priceSabaSaba $0.30 · Gemma 2 27B $0.65 · Mixtral 8x22B Instruct $3.00 per 1M tokens (3:1 blend)
- Longest contextMixtral 8x22B InstructMixtral 8x22B Instruct 65,536 · Saba 32,768 · Gemma 2 27B 8,192 tokens
- Widest inputsMixtral 8x22B Instruct and SabaMixtral 8x22B Instruct: Text, PDFs · Saba: Text, PDFs · Gemma 2 27B: Text
- Self-hostingMixtral 8x22B Instruct and Gemma 2 27BPublishes downloadable weights
| Measure | Weight | Mixtral 8x22B Instruct | Saba | Gemma 2 27B |
|---|---|---|---|---|
| Price | 50% | 26 | 72 | 56 |
| Inputs & features | 30% | 35 | 35 | 0 |
| Context window | 20% | 12 | 0 | 0 |
| Overall | 100% | 26/100 | 46/100 | 28/100 |
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 | Mixtral 8x22B InstructMistral AI | SabaMistral AI | Gemma 2 27BGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.0 | — | 122.1 (best) |
| ECI rank | #155 of 165 | — | #152 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 34.1% | — | 36.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 1.4% |
| Price per million tokens | |||
| Input | $2.00 | $0.20 (best) | $0.65 |
| Output | $6.00 | $0.60 (best) | $0.65 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.30 (best) | $0.65 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 4 providers | Median of 3 providers |
| Limits | |||
| Context window | 65,536 tokens (best) | 32,768 tokens | 8,192 tokens |
| Max output | 52,428 tokens (best) | 26,214 tokens | 2,048 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | Yes | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | — | — |
| API providers | 5 (best) | 4 | 3 |
| Released | Apr 17, 2024 | Feb 17, 2025 | Jul 13, 2024 |
| Knowledge cutoff | Jan 31, 2024 | Sep 30, 2024 | Jun 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mixtral 8x22B Instruct$32.00
- Saba$3.20
- Gemma 2 27B$7.80
Which should you choose?
Which is better: Mixtral 8x22B Instruct, Saba or Gemma 2 27B?
Saba is the better all-round choice, scoring 46/100 against Gemma 2 27B (28) and Mixtral 8x22B Instruct (26). It leads on price. Mixtral 8x22B Instruct 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, Mixtral 8x22B Instruct, Saba or Gemma 2 27B?
Saba is cheaper at $0.20 input / $0.60 output per million tokens (median across 4 API providers). Gemma 2 27B costs $0.65 input / $0.65 output per million tokens (median across 3 API providers); Mixtral 8x22B Instruct costs $2.00 input / $6.00 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.30 per million tokens for Saba versus $0.65 for Gemma 2 27B (2.2× as much) and $3.00 for Mixtral 8x22B Instruct (10× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mixtral 8x22B Instruct has an ECI of 122.0, Saba has not been scored yet and Gemma 2 27B has an ECI of 122.1.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x22B Instruct, Saba and Gemma 2 27B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Gemma 2 27B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Mixtral 8x22B Instruct has the largest context window at 65,536 tokens, against 32,768 for Saba and 8,192 for Gemma 2 27B. Maximum output per response: Mixtral 8x22B Instruct up to 52,428, Saba up to 26,214, Gemma 2 27B up to 2,048 tokens.
Which can read images, PDFs, audio or video?
Mixtral 8x22B Instruct accepts text and PDFs; Saba accepts text and PDFs; Gemma 2 27B accepts text. Mixtral 8x22B Instruct handles the widest range of inputs.
Are any of these open source?
Mixtral 8x22B Instruct and Gemma 2 27B publishes its weights and can be self-hosted; Saba is proprietary.
Which is newer?
Saba is the newest, released Feb 17, 2025. Gemma 2 27B came out Jul 13, 2024; Mixtral 8x22B Instruct came out Apr 17, 2024. Knowledge cutoff: Mixtral 8x22B Instruct Jan 31, 2024, Saba Sep 30, 2024, Gemma 2 27B Jun 30, 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.
