Codestral latest vs Mistral Small latest
Codestral latest (Google Vertex AI, 128,000-token context) versus Mistral Small latest (Mistral AI, 262,144-token context). Mistral Small latest is cheaper by 6% on a blended token mix. Mistral Small latest uniquely supports vision input and structured output (json schema). Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Codestral latest vs Mistral Small latest
Codestral latest and Mistral Small latest are priced within 6% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
Mistral Small latest ships a 262,144-token context window, 2.0x larger than Codestral latest's 128,000 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 128,000 tokens, the extra context on Mistral Small latest is insurance you may never use — and Codestral latest may win on other axes.
On capability surface area, the models diverge: Mistral Small latest supports vision input where the other does not; Mistral Small latest supports structured output (json schema) where the other does not; Mistral Small latest supports native reasoning mode where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: mistral-small-latest
provider: mistral
fallback:
model: codestral-latest
provider: vertex-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Codestral latest | Mistral Small latest | |
|---|---|---|
| Input price | $0.200/M | $0.150/M |
| Output price | $0.600/M | $0.600/M |
| Context window | 128,000 | 262,144 |
| Max output | 128,000 | 262,144 |
| Function calling | ✓ | ✓ |
| Vision | — | ✓ |
| Audio input | — | — |
| Reasoning | — | ✓ |
| Prompt caching | — | — |
| Structured output | — | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Codestral latest | Mistral Small latest | Delta |
|---|---|---|---|
| Startup 10K requests/day | $96.00 /mo | $81.00 /mo | $15.00/mo |
| Mid-market 100K requests/day | $960 /mo | $810 /mo | $150/mo |
| Enterprise 1M requests/day | $9,600 /mo | $8,100 /mo | $1,500/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — Mistral Small latest fits 262,144 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your inputs include screenshots, diagrams, or product photos — Mistral Small latest accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — Mistral Small latest ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Codestral latest, switching to Mistral Small latest means re-architecting that path (and vice versa).
- • Vision input
- • Structured output (JSON schema)
- • Native reasoning mode
Capabilities both share (2)
- ✓ Function calling
- ✓ Streaming
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 105% when moving from Codestral latest (128,000) to Mistral Small latest (262,144). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 128,000 on Codestral latest vs 262,144 on Mistral Small latest. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Mistral Small latest has capabilities Codestral latest lacks: Vision input, Structured output (JSON schema), Native reasoning mode. Worth wiring through the agent design before commit.
- Provider changes from Google Vertex AI to Mistral AI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Codestral latest vs Mistral Small latest in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Codestral latest primary, mirror 20% of traffic to Mistral Small latest in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Codestral latest vs Mistral Small latest
Which is cheaper, Codestral latest or Mistral Small latest? ▾
Mistral Small latest is cheaper by roughly 6% on a blended input + output token mix. Input prices are $0.200/M for Codestral latest versus $0.150/M for Mistral Small latest; output prices are $0.600/M versus $0.600/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Codestral latest versus Mistral Small latest? ▾
Codestral latest supports up to 128,000 tokens of context. Mistral Small latest supports up to 262,144 tokens. Mistral Small latest has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Codestral latest and Mistral Small latest both support tool calling? ▾
Yes — both Codestral latest and Mistral Small latest support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Codestral latest and Mistral Small latest process images? ▾
Mistral Small latest accepts native image input. Codestral latest does not — you would need to route image-heavy workloads through Mistral Small latest or add a separate vision model in front of Codestral latest.
When should I choose Codestral latest over Mistral Small latest? ▾
On the data this page surfaces, Codestral latest is the right pick when Mistral Small latest's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
When should I choose Mistral Small latest over Codestral latest? ▾
Your workload needs long context — Mistral Small latest fits 262,144 tokens versus the other model's 128,000, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your inputs include screenshots, diagrams, or product photos — Mistral Small latest accepts image input natively, the other doesn't. Your tasks involve multi-step planning or math-heavy reasoning — Mistral Small latest ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
How do I A/B test Codestral latest against Mistral Small latest in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.