LLM Reference

Together AI - Gemma 3n-e4B vs Mistral Large 2.1 (2411)

Together AI - Gemma 3n-e4B (2026) and Mistral Large 2.1 (2411) (2024) are compact production models from Google DeepMind and MistralAI. Together AI - Gemma 3n-e4B ships a 8k-token context window, while Mistral Large 2.1 (2411) ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.

Mistral Large 2.1 (2411) fits 16x more tokens; pick it for long-context work and Together AI - Gemma 3n-e4B for tighter calls.

Decision scorecard

Local evidence first
SignalTogether AI - Gemma 3n-e4BMistral Large 2.1 (2411)
Best fortool-calling agentstool-calling agents
Decision fitAgents, Classification, and JSON / Tool useRAG, Agents, and Long context
Context window8k128k
Cheapest output$0.04/1M tokens-
Provider routes1 tracked0 tracked
Shared benchmarks0 shared0 shared

Decision tradeoffs

Choose Together AI - Gemma 3n-e4B when...
  • Together AI - Gemma 3n-e4B has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Together AI - Gemma 3n-e4B for Agents, Classification, and JSON / Tool use.
Choose Mistral Large 2.1 (2411) when...
  • Mistral Large 2.1 (2411) has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Local decision data tags Mistral Large 2.1 (2411) for RAG, Agents, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Together AI - Gemma 3n-e4B

$26.00

Cheapest tracked route/tier: Together AI

Mistral Large 2.1 (2411)

Unavailable

No complete token price in local provider data

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Together AI - Gemma 3n-e4B -> Mistral Large 2.1 (2411)
  • No overlapping tracked provider route is sourced for Together AI - Gemma 3n-e4B and Mistral Large 2.1 (2411); plan for SDK, billing, or endpoint changes.
Mistral Large 2.1 (2411) -> Together AI - Gemma 3n-e4B
  • No overlapping tracked provider route is sourced for Mistral Large 2.1 (2411) and Together AI - Gemma 3n-e4B; plan for SDK, billing, or endpoint changes.

Specs

Specification
Released2026-03-152024-11-18
Context window8k128k
Parameters4B123B
ArchitectureDecoder OnlyDecoder Only
LicenseGemmaMistral License
OpennessOpen weightsOpen weights
Commercial useCommercial use: conditionalCommercial use: non-commercial
Knowledge cutoff2024-06-

Pricing and availability

Pricing attributeTogether AI - Gemma 3n-e4BMistral Large 2.1 (2411)
Input price$0.02/1M tokens-
Output price$0.04/1M tokens-
Providers-

Capabilities

CapabilityTogether AI - Gemma 3n-e4BMistral Large 2.1 (2411)
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingYesYes
Tool useYesYes
Structured outputsYesYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark scores are currently available for this pair.

Deep dive

The capability footprint is close: both models cover function calling, tool use, and structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

Pricing coverage is uneven: Together AI - Gemma 3n-e4B has $0.02/1M input tokens and Mistral Large 2.1 (2411) has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Together AI - Gemma 3n-e4B when provider fit and broader provider choice are central to the workload. Choose Mistral Large 2.1 (2411) when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, Together AI - Gemma 3n-e4B or Mistral Large 2.1 (2411)?

Mistral Large 2.1 (2411) supports 128k tokens, while Together AI - Gemma 3n-e4B supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Together AI - Gemma 3n-e4B or Mistral Large 2.1 (2411) open source?

Together AI - Gemma 3n-e4B is listed under Gemma. Mistral Large 2.1 (2411) is listed under Mistral License. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for function calling, Together AI - Gemma 3n-e4B or Mistral Large 2.1 (2411)?

Both Together AI - Gemma 3n-e4B and Mistral Large 2.1 (2411) expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Which is better for tool use, Together AI - Gemma 3n-e4B or Mistral Large 2.1 (2411)?

Both Together AI - Gemma 3n-e4B and Mistral Large 2.1 (2411) expose tool use. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Which is better for structured outputs, Together AI - Gemma 3n-e4B or Mistral Large 2.1 (2411)?

Both Together AI - Gemma 3n-e4B and Mistral Large 2.1 (2411) expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Where can I run Together AI - Gemma 3n-e4B and Mistral Large 2.1 (2411)?

Together AI - Gemma 3n-e4B is available on Together AI. Mistral Large 2.1 (2411) is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-06-16. Data sourced from public model cards and provider documentation.